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Tree-CNN: A hierarchical Deep Convolutional Neural Network for incremental learning
Open accessAbstract
Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. Adding new information to such a model presents a challenge due to complex training issues, such as "catastrophic forgetting", and sensitivity to hyper-parameter tuning. However, in this modern world, data is constantly evolving, and our deep learning models are required to adapt to these changes. In this paper, we propose an adaptive hierarchical network structure composed of DCNNs that can grow and learn as new data becomes available. The network grows in a tree-like fashion to accommodate new classes of data, while preserving the ability to distinguish the previously trained classes. The network organizes the incrementally available data into feature-driven super-classes and improves upon existing hierarchical CNN models by adding the capability of self-growth. The proposed hierarchical model, when compared against fine-tuning a deep network, achieves significant reduction of training effort, while maintaining competitive accuracy on CIFAR-10 and CIFAR-100.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Tree-CNN is a hierarchical DCNN that grows tree-like as new classes arrive, assigning new classes to existing, merged, or new child nodes based on softmax likelihood and retraining only affected nodes. On CIFAR-10 and CIFAR-100 it reduces training effort versus fine-tuning a baseline network while maintaining competitive accuracy.
Key findings
- On CIFAR-10, after adding 4 new classes, Tree-CNN reached 86.24% test accuracy with normalized training effort 0.60, versus baseline B:V at 90.51% accuracy and effort 1.00; Tree-CNN used 40% less effort than B:V and 30% less than B:II, while being 3.76% below B:V.
- On CIFAR-100 over 9 incremental stages, Tree-CNN-5/10/20 achieved final test accuracies of 61.57%, 60.46%, and 59.99% (average 69.85%, 69.53%, 68.49%), compared with B:V at 63.05% final / 72.23% average, iCaRL at 49.11% / 64.10%, and LwF at 25.00% / 44.49%. The paper reports Tree-CNNs yield 10% higher accuracy than iCaRL and over 50% higher than LwF.
- Tree-CNNs required lower training effort than fine-tuning cases B:II–B:V; B:I had the lowest effort but the worst accuracy degradation. Tree-CNN-5 grew to 3.4× the size of Network B, Tree-CNN-10 to 2.2×, and Tree-CNN-20 to 1.8×.
- The initial CIFAR-10 Tree-CNN achieved 98.73% root-node testing accuracy, 86% for the “Animals” branch, 94.43% for “Vehicles,” and 89.10% overall.
Methods
- Tree-CNN is composed of DCNNs in a tree: the root classifies inputs into child nodes, branch nodes perform finer classification, and each leaf node is uniquely associated with one class. Incremental learning samples 10% of new-class training images, computes a softmax likelihood matrix, orders classes, then adds each new class to an existing child, merges child nodes, or adds a new child; affected nodes are retrained with old and new data.
- Experiments used CIFAR-10 (6 initial classes, then 4 new classes) and CIFAR-100 (100 classes, 500 training and 100 testing images per class, randomly divided into 10 groups of 10 and introduced over 9 stages). CIFAR-100 used max depth 2, thresholds 0.1, and maxChildren 5, 10, or 20. Baseline Network B was a VGG-like 11-layer network fine-tuned at five depths (B:I–B:V).
- Metrics were testing accuracy and training effort, defined as the sum over nets of total weights × total training samples. Training used MatConvNet, mini-batch SGD with momentum 0.9, dropout, batch normalization, weight decay 0.001, 300 epochs, and learning rate 0.1 for 200 epochs then reduced by 10 every 50 epochs.
Limitations
- The paper states Tree-CNN continues to grow in size over time, and the implications for memory requirements need investigation; it also notes there is no standardized benchmark protocol for incremental learning, so it used a protocol similar to iCaRL.
- The paper states branch nodes have a higher chance of overfitting as dataset size per node shrinks, so more dropout layers were added; it also notes that if the root node misclassifies an input, the final classification is incorrect regardless of the branch node’s output, and old data must be shown during retraining to avoid catastrophic forgetting.
- Evident from the text: evaluation is limited to CIFAR-10 and CIFAR-100; final CIFAR-100 accuracy for Tree-CNN-5 (61.57%) remains below B:V (63.05%) and below HD-CNN (67.38%) and Hertel et al. (67.68%) as listed in Table 7.
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33 Own interpretation · 22 Own result · 10 Not checked yet · 5 No basis given · 3 Own method
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“The size of Network B remains relatively unchanged, as only additional output nodes are added, which translates to a small fraction of new weights in the final layer.”
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“However, it suffers the worst accuracy degradation over the 9 learning stages (Fig. 6b).”
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“The proposed hierarchical model, when compared against fine-tuning a deep network, achieves significant reduction of training effort, while maintaining competitive accuracy on CIFAR-10 and CIFAR-100.”
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“At the end of the nine incremental learning stages, certain similar objects grouped together is shown in Fig. 8 for Tree-CNN-10 .”
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“We observe that as new classes are added, the Tree-CNNs grow in size by adding more branches (Fig. 5a).”
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“We observe that retraining only the FC layers of baseline network ( B:I ) requires the least training effort, however, it gives us the lowest accuracy of 78 : 37% .”
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“The root node achieves a testing accuracy of 98 : 73% , while the branch nodes, 'Animals' and 'Vehicles', achieve 86% and 94 : 43% testing accuracy respectively.”
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“The Tree-CNNs exhibit a lower training effort than 4 fine-tuning test cases, B:II - B:V .”
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“Fig. 6b shows that all the networks suffer from some accuracy degradation with increasing number of classes.”
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“An interesting thing to note was similar looking classes, that were also semantically similar, were grouped under the same branches.”
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“However, the Tree-CNN continues to grow in size over time, and the implications of that on memory requirements needs to be investigated.”
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“At the end of 9 incremental learning stages, the root node of Tree-CNN-5 had 23 branch nodes and 3 leaf nodes.”
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“As expected, the root node of Tree-CNN-20 had least number of child nodes, 9 branch nodes and 3 leaf nodes.”
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“As maxChildren is reduced, the test accuracy improves, as observed in Fig. 5b.”
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“Overall, the network achieves a testing accuracy of 89 : 10% .”
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“The training effort for the 3 Tree-CNNs was almost identical, within 1e-2 margin of each other (Fig. 6a), over the 9 incremental learning stages.”
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“the test case B:I has a significantly lower training effort than all the other cases, as it only retrains the final fully connected layer.”
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“For all the models, the training effort required at a particular learning stage was greater than the effort required by the previous stage.”
- Also stated in: § 6. Discussion · p. 21 · plain
“We observed that each incremental learning stage required more effort than the previous, because images belonging to old classes needed to be shown to the CNNs.”
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- Also stated in: § 6. Discussion · p. 21 · plain
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“Whereas, the root node of Tree-CNN-10 has 12 branch nodes and 5 leaf nodes.”
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“The proposed hierarchical model, when compared against fine-tuning a deep network, achieves significant reduction of training effort, while maintaining competitive accuracy on CIFAR-10 and CIFAR-100.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“We observe that retraining only the FC layers of baseline network ( B:I ) requires the least training effort, however, it gives us the lowest accuracy of 78 : 37% .”
plainn = 5- Asserted: passes — The authors put it forward as true.
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In the paper
“When a trained DCNN is retrained exclusively over new data, it results in the destruction of existing features learned from earlier data.”
What the sources say
- Not checked yetcites [8] Ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift
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In the paper
“Another problem of incrementally training a DCNN is the issue of 'catastrophic forgetting' [8].”
- Also stated in: § Abstract · p. 1 · plain
“Adding new information to such a model presents a challenge due to complex training issues, such as 'catastrophic forgetting', and sensitivity to hyperparameter tuning.”
What the sources say
- Not checked yetcites [8] Ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift
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- Also stated in: § Abstract · p. 1 · plain
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In the paper
“To avoid catastrophic forgetting, and to leverage the features learned in previous task, this work proposes a network made of CNNs that grows hierarchically as new classes are introduced.”
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- Rests on an assumption: In the real world, data is gathered incrementally over time and all information is not available at once.
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In the paper
“The network adds the new classes like new leaves to the hierarchical structure.”
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“The branching is based on the similarity of features between new and old classes.”
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In the paper
“The branch node has a higher chance of over-fitting than the root node as the dataset per node shrinks in size as we move deeper into the tree.”
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In the paper
“If an incorrect classification happens at Root Node, for example it classifies an image of a car into the 'Animal Node' (CIFAR-10 example, Sec 4.1), irrespective of what the branch node classifies it as, it would still be an incorrect classification.”
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“During inference, a single node is evaluated at a time, thus the memory requirement per node inference is much lower than the size of the entire model.”
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“Our proposed method offers the best trade-off between accuracy and training effort when compared against fine-tuning layers of a deep network.”
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In the paper
“Our proposed method, Tree-CNN, thus offers a better learning model that is based on hierarchical classifiers and transfer learning and can organically adapt to new information over time.”
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In the paper
“Tree-CNN grows in a manner such that images that share common features are grouped together.”
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In the paper
“From Fig. 6, we can conclude that Tree-CNNs offer the most optimal trade-off between training effort and testing accuracy.”
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In the paper
“Such a model allows us to leverage the convolution layers learned previously to be used in the new bigger network.”
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In the paper
“Tree-CNNs are able to achieve near state-of-the-art accuracy for CIFAR-100 as illustrated in Table 7.”
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In the paper
“This accuracy vs training effort trade-off is presented in Fig. 4b, where it is clearly visible that Tree-CNN provided the most optimal solution for adding the 4 new classes.”
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“This is because we had to show images belonging to old classes to avoid 'catastrophic forgetting'.”
plaindirectn = 5- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Catastrophic forgetting is an inherent problem in deep neural networks.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this paper, we propose an adaptive hierarchical network structure composed of DCNNs that can grow and learn as new data becomes available.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“This shows that our learning method using the hierarchical structure is more resistant to catastrophic forgetting as new classes are added.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: Catastrophic forgetting is an inherent problem in deep neural networks.
- It may state two separate claims as one. 23
- Probably not a substantial claim. 12
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Tree-CNN-5 almost grows 3 : 4 the size of Network B , while Tree-CNN-10 and Tree-CNN-20 reach 2 : 2 and 1 : 8 the baseline size, respectively.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The root node achieves a testing accuracy of 98 : 73% , while the branch nodes, 'Animals' and 'Vehicles', achieve 86% and 94 : 43% testing accuracy respectively.”
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
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In the paper
“While there were some groups that had object sharing semantic similarity as well, there were odd groups as well, such as Node 13 as shown in Fig. 8.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The three Tree-CNNs perform almost at par with B:IV , and outperform all other variants of network B .”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The Tree-CNN has the second lowest normalized training effort, 40% less than B:V , and 30% less than B:II .”
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It may state two separate claims as one.
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In the paper
“At the same time, Tree-CNN had comparable accuracy to B:II and B:III , while just being less than the ideal case B:V by a margin of 3 : 76% .”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“In recent years Deep Convolutional Neural Networks (DCNNs) have emerged as the leading architecture for large scale image classification [1].”
What the sources say
- Not checked yetcites [1] Aljundi, 2016, Expert gate: Lifelong learning with a network of experts, CoRR
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The initial nodes of the Tree-CNN assign the input into coarse super-classes, and as we approach the leaves of the network, finer classification is done.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While this example is for a two level Tree-CNN, the algorithm can potentially be extended to deeper Tree-CNN models.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“However, in these works, the dataset is fixed from the beginning, and prior knowledge of all the classes and their properties is used to build a hierarchical model.”
What the sources say
- Not checked yetcites [22] Sarwar, S. S., Ankit, A., & Roy, K. (2017). Incremental learning in deep convolutional neural networks using partial network sharing, arXiv preprint arXiv:1712.02719.
- Not checked yetcites [23] Sarwar, 2017, Gabor Filter assisted energy efficient fast learning convolutional neural networks
- Not checked yetcites [24] Shmelkov, 2017, Incremental learning of object detectors without catastrophic forgetting
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The decision on how to grow the tree is semi-supervised: the algorithm itself decides how to grow the tree, given the constraints by the user.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Each leaf node is uniquely associated to a class and no two leaf nodes have the same class.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Images belonging to the older dataset are required during retraining, but by localizing the change to a small section of the whole network, our method tries to reduce the training effort and complexity.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“It has been observed that initial layers of a CNN learn very generic features [17] that has been exploited for transfer learning [18]' [19].”
What the sources say
- Not checked yetcites [17] Panda, 2017, Semantic driven hierarchical learning for energy-efficient image classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This shows that only retraining the final linear classifier, i.e. the fully connected layer, is not sufficient. We need to train the feature extractors, i.e. convolutional blocks, as well on the new data.”
plaindirectn = 5- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, it can learn with lower training effort than fine-tuning a deep network, while preserving much of the accuracy.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To compare against the proposed Tree-CNN , we defined a baseline network (Network B ) with a complexity level similar to two stage Tree-CNN .”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Such a method of training and classification can be used to hierarchically classify large datasets.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Once the new classes are allotted locations in the tree, supervised gradient descent based training is performed on the modified/new nodes. This saves us from modifying the whole network, and only affected portions of the network require retraining/fine-tuning.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The final leaf nodes, and the distance between them can also be used as a measure of how similar any two images are.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The network organizes the incrementally available data into feature-driven superclasses and improves upon existing hierarchical CNN models by adding the capability of self-growth.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In 'iCarl' [16], a ResNet-32 is retrained with new data and only 2000 samples of old data (called 'exemplars') at every stage. It is able to recover a good amount performance, compared to 'LwF' but still falls short of state-of-the-art by 18%.”
What the sources say
- Not checked yetcites [16] Panda, 2017, FALCON: Feature driven selective classification for energy-efficient image recognition, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems source
plaindirectn = 2,000- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The hierarchical node-based learning model of the Tree-CNN attempts to confine the change in the model to a few nodes only. And, in this way, it limits the computation costs of retraining, while using all the previous data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Such hierarchical CNN models have been shown to perform at par or even better than standard DCNNs [22].”
What the sources say
- Not checked yetcites [22] Sarwar, S. S., Ankit, A., & Roy, K. (2017). Incremental learning in deep convolutional neural networks using partial network sharing, arXiv preprint arXiv:1712.02719.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Every class is finally associated with a unique leaf node, hence leaf nodes do not require a look up table.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Training Effort attempts to capture the number of weight updates that happen per training epoch.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“For 'LwF', a ResNet-32 [32] is retrained exclusively on new data at every stage. Hence it suffers the most accuracy degradation.”
What the sources say
- Not checked yetcites [32] Yosinski, 2014, How transferable are features in deep neural networks?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The network grows in a tree-like fashion to accommodate new classes of data, while preserving the ability to distinguish the previously trained classes.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In our work, Tree-CNN starts out as a single root node and generates new hierarchies to accommodate the new classes.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At every incremental learning stage, the root node is trained on all the available data as it needs to learn to classify all the objects into the new branches.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The Tree-CNN generates hierarchical grouping of initially unrelated classes, thereby generating a label relation graph out of these classes[33].”
What the sources say
- Not checked yetcites [33]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As batch size and number of training epochs is kept the same, the product of the number of weights and the number of training samples used can provide us with the measure of the computation cost of a learning stage.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Inspired from hierarchical classifiers, our proposed model, Tree-CNN is composed of multiple nodes connected in a tree-like manner. Each node (except leaf nodes) has a DCNN which is trained to classify the input to the node into one of it's children.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Common features, that are shared between images, have been used previously to build hierarchical classifiers. These features can be grouped semantically, such as in [20], or be feature-driven, such as 'FALCON' [21].”
What the sources say
- Not checked yetcites [20] Russakovsky, 2015, Imagenet large scale visual recognition challenge, International Journal of Computer Vision source
- Not checked yetcites [21] Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., & Kavukcuoglu, K., et al. (2016). Progressive neural networks, arXiv preprint arXiv:1606.04671.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
In the real world, data is gathered incrementally over time and all information is not available at once.
“However, in real world, we hardly have all the information at once, and data is, instead, gathered incrementally over time.”
The authors state this as an assumption.
The starting model is already trained to recognize a certain number of objects.
“We start with the assumption that we have a model that is already trained to recognize a certain number of objects.”
The authors state this as an assumption.
The system has access to all the data that has been introduced so far during fine-tuning.
“It is assumed that the system has access to all the data that has been introduced so far.”
The paper relies on this without support.
There is an absence of a standardized benchmark protocol for incremental learning.
“There is an absence of standardized benchmark protocol for incremental learning, which led us to use a benchmarking protocol similar to one used in iCaRL [16].”
The paper relies on this without support.
The dataset available to the user has unique labels assigned to each of its object classes.
“The dataset available to the user will have unique labels assigned to each of it's object classes.”
The paper relies on this without support.
Adding new image classes to a network should be easier than retraining the whole network again with all the classes.
“The motivation of this work stems from the idea that subsequent addition of new image classes to a network should be easier than retraining the whole network again with all the classes.”
The paper relies on this without support.
Catastrophic forgetting is an inherent problem in deep neural networks.
“This is due to the inherent problem of 'catastrophic forgetting' in deep neural networks.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Although this method mitigates catastrophic forgetting to a certain extent, it is intuitively a method that consumes more space and is not efficient in training.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [28]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 5.2. Sequentially adding new classes (CIFAR-100) · p. 19
“The three Tree-CNNs perform almost at par with B:IV , and outperform all other variants of network B .”
§ Appendix C. Full Simulation Results · p. 27
“| 100 | 31.73 | 48.68 | 54.16 | 60.48 | 63.05 | 61.57 | 60.46 | 59.99 |”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
(2014) · Cited by 33,419
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Cited by
Sorted by citations
Online continual learning in image classification: An empirical survey
Mai, Z., Li, R., Jeong, J., Quispe, D., Kim, H., & Sanner, S. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.10.021 · Cited by 468
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
Shu, D., Park, S., & Kwon, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00396 · Cited by 284
Incremental Learning Techniques for Semantic Segmentation
Michieli, U., & Zanuttigh, P. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00400 · Cited by 274
“Some methods [16, 27, 28, 36] exploit network architectures which grow during the training process.”
MethodAutomated emotion recognition: Current trends and future perspectives
Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Barua, P. D., Murugappan, M., Chakole, Y., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106646 · Cited by 172
Crop mapping from image time series: Deep learning with multi-scale label hierarchies
Türkoglu, M. Ö., D’Aronco, S., Perich, G., Liebisch, F., Streit, C., Konrad Schindler, B., & Wegner, J. D. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112603 · Cited by 194
Metadata
- Authors
- Roy, D., Panda, P., & Roy, K.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Roy, Deboleena
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 121
- Pages
- 148-160
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2804177786
- Semantic Scholar
- e811c76173d4b0e478d3ab5965358a6377dd48ce
- Cited by
- 257
- References
- 37
- Citations by year
- 2026: 12 · 2025: 29 · 2024: 39 · 2023: 44 · 2022: 41 · 2021: 53 · 2020: 30
- FWCI
- 15.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neural Networks (acceptedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Semiconductor Research Corporation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Keywords
- incremental learning, catastrophic forgetting, hierarchical convolutional neural network, class-incremental learning, adaptive network structure
- MeSH terms
- Deep Learning, Animals, Humans, Pattern Recognition, Automated, Photic Stimulation, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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