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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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In the paper
“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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In the paper
“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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- It may state two separate claims as one. 23
- Probably not a substantial claim. 12
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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.”
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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.”
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“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.”
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“The three Tree-CNNs perform almost at par with B:IV , and outperform all other variants of network B .”
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“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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“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% .”
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“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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“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.”
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“While this example is for a two level Tree-CNN, the algorithm can potentially be extended to deeper Tree-CNN models.”
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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
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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.”
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In the paper
“Each leaf node is uniquely associated to a class and no two leaf nodes have the same class.”
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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.”
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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
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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.”
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“Thus, it can learn with lower training effort than fine-tuning a deep network, while preserving much of the accuracy.”
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“To compare against the proposed Tree-CNN , we defined a baseline network (Network B ) with a complexity level similar to two stage Tree-CNN .”
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In the paper
“Such a method of training and classification can be used to hierarchically classify large datasets.”
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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.”
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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.”
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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.”
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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
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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.”
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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.
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In the paper
“Every class is finally associated with a unique leaf node, hence leaf nodes do not require a look up table.”
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In the paper
“Training Effort attempts to capture the number of weight updates that happen per training epoch.”
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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?
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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.
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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.
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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.”
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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.
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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.”
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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.
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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
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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
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Gradient-based learning applied to document recognition
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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
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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
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Pan, Y.-Y., Zhang, H., Yang, J., Guo, J. M., Yang, Z., Wang, J., & Song, G. (2021) · Contrast Media & Molecular Imaging · doi:10.1155/2021/7598613 · Cited by 1
“ey are extensively used in image segmentation, image classification, image registration, and target detection [16, 17].”
BackgroundGrafting Heterogeneous Neural Networks for a Hierarchical Object Classification
Heller, G., Perrin, E., Vrabie, V. D., Dusart, C., & Roux, S. L. (2022) · IEEE Access · doi:10.1109/access.2022.3144579 · Cited by 1
“The Tree-CNN [26] is an architecture that is not subject to this constraint of consistency between the feature maps.”
BackgroundMethodHuang, L., Yi-gong, Z., & Yang, T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12651-3 · Cited by 1
“Although our method resembles those proposed in [21, 30] in terms of dynamically growing a network to continuously improve CNN classification performance, critical differences exist.”
BackgroundMethod(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.1231.54 · Cited by 1
Large-scale deep class-incremental learning
Belouadah, E. (2021) · theses.fr (ABES) · doi:10.70675/427ce7e6z19e6z48e8za9ddz81eebeef3f01 · Cited by 1
Introducing Curvature to the Label SpacePreprint
Sheehan, C., Day, B., & Lió, P. (2018) · arXiv (Cornell University) · doi:10.17863/cam.40293
Dynamic MLML-tree based adaptive object detection using heterogeneous data distribution
Shin, D. K., Ahmed, M. U., Kim, Y. H., & Rhee, P. K. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08285-7
“Recently, deep learning object classification based on a course-to-fine-feature hierarchy can be found [24, 32].”
BackgroundA VCA-Based Approach to Enhance Learning Data Sets for Object Classification
Baran, R., & Zeja, A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-59000-0_22
Brick Assembly Networks: An Effective Network for Incremental Learning Problems
Ho, J., & Kang, D.-K. (2020) · Electronics · doi:10.3390/electronics9111929
“[17] have proposed a hierarchical deep convolutional neural network (TreeCNN) for solving the incremental learning problem by growing a trained network structure if new labels are added to the network.”
BackgroundInfluentialAILearn: An Adaptive Incremental Learning Model for Spoof Fingerprint DetectionPreprint
Agarwal, S., Rattani, A., & Chowdary, C. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14639
“Incremental learning can be applied in various applications such as credit card fraud detection [7, 8], object recognition in image processing [9], video surveillance in computer vision [10], interactive kinesthetic teaching in robotics [11], automated annotation for video and speech tagging [12], science article reco…”
BackgroundDigital Taxonomist: Identifying Plant Species in Citizen Scientists' PhotographsPreprint
de Lutio, R., She, Y., D’Aronco, S., Russo, S., Brun, P., Wegner, J. D., & Schindler, K. (2021) · arXiv (Cornell University)
Wang, Y., Yu, S., Yang, X., & Shen, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02824
“Roy et al. [26] proposed a CNN with tree structure, which is built by growing the CNN in a tree-like fashion, to deal with data with unseen classes.”
Research on Traffic Analysis System Based on Machine LearningPreprint
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SIS-CNN: Semantic Image Segmentation Using Convolutional Neural Networks
Tahir, M. A. A., Xiao, F., & Shaker, Z. (2021) · International Journal of Advanced Network Monitoring and Controls · doi:10.21307/ijanmc-2021-022
“The CNN follows a hierarchical model [29] that works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to eachother and the output is processed.”
BackgroundOnline Continual Learning in Image Classification: An Empirical SurveyPreprint
Mai, Z., Li, R., Jeong, J., Quispe, D., Kim, H., & Sanner, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10423
Exploring Deep Neural Networks for Plant Image Classification
Bafandkar, M. (2022) · doi:10.36939/ir.202205031043
Idt: An Incremental Deep Tree Framework for Biological Image ClassificationPreprint
Wafa, M. R. A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4008737
GraftNet: An Efficient and Flexible Multi-label Image Classification and Its Application
(2021) · International Workshop on Computer Science and Engineering (WCSE) · doi:10.18178/wcse.2021.06.011
Improve the Performance and Stability of Incremental Learning by a Similarity Harmonizing Mechanism
Ma, J., Liao, M., & Zhang, L. (2022) · IEEE Access · doi:10.1109/access.2022.3219502
Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection
Suttanuruk, J., Jomnonkwao, S., Ratanavaraha, V., & Kanjanawattana, S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.033900
A New Approach to Enhance Learning Data Sets for Object Classification
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A Multi-granularity Decision Fusion Method Based on Category Hierarchy
Mi, J., Huang, K.-Y., & Li, N. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-4742-3_12
A Fully Distributed Training for Class Incremental Learning in Multihead Networks
Dai, M., Kong, Y., Zhong, J., Zhang, S., & Wang, H. (2023) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps57453.2023.10225999
“Note that Tree-CNN also has Basenet and branches, with 978-1-6654-9427-4/23/$31.00 ©2023 IEEE IE EE IN FO CO M 2 02 3 -”
BackgroundInfluentialAnimal Intrusion Detection Using Deep Learning
Varshini Sri, S., & C, S. S. R. (2023) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem25827
A Hierarchy-Aware Deep Capsule Network for Multi-Label Image ClassificationPreprint
Noor, K. T., Robles‐Kelly, A., Zhang, L. Y., Bouadjenek, M. R., & Luo, W. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4641400
ITCNN: Incremental Learning Network Based on ITDA and Tree Hierarchical CNN
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Hierarchical Crop Mapping from Satellite Image Sequences with Recurrent Neural Networks
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Continual Deep Reinforcement Learning for Financial Trading Using Bi-Directional Knowledge TransferPreprint
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DualViT: A Hierarchical Vision Transformer for Broad and Fine Class Embeddings
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Class Incremental Learning with Forward Memory
Dai, M., Hu, M., Kong, Y., & Gu, Y. (2024) · IEEE/ACIS International Conference on Computer and Information Science (ICIS) · doi:10.1109/icis61260.2024.10778395
“Meanwhile, [22], [23] have utilized similar multi-head network structures.”
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CapT: A Hierarchical Capsule Representation Learning Approach for Class Continual Learning
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“42 Tree-CNN [16] old samples for re-training, the average incremental accuracy decreases with in the of classes.”
Multiple Additive Neural Networks for Structured and Unstructured Data
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“[12] introduces a concept for assembling hierarchical configurations of multiple neural networks arranged in a tree-like fashion that can expand to accommodate new data.”
IoT Attack Detection Based on Continuous Learning Across Multiple Regions
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Xu, Z., Yang, S., Xu, B., Shen, F., & Zhao, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5342329
A Joint Detection Network for Snowy Weather with Image Recovery and Domain Adaptation
Zhou, G., Wang, Z., Xue, M., Yang, S., Zhou, Y., & Mo, J. (2025) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ieeeconf65522.2025.11136843
“The framework comprises two main parts: (1) the Progressive Recovery Module, featuring the PRN r [8] network, and (2) the Object Detection Module, which includes YOLOv8 [14] and a hierarchical domain adaptation network.”
A Web-Based Platform for Korean Calligraphy Learners Using Deep Learning
Yang, C., Osman, M. A., & Sarvghadi, M. A. (2025) · IEEE Access · doi:10.1109/access.2025.3612578
“Besides, CNNs can learn hierarchical representations of the data, which means they can learn to recognize increasingly complex patterns in the input data, even if they are distorted or partially obscured [31].”
Collie: Federated Query Processing via Reinforcement Learning
Hu, G., Yao, X., & Yu, Y. (2025) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa67752.2025.00177
“We use TreeCNN [24] as the backbone, which is aware of the structural information from feature vector tree.”
Deep Hierarchy-Aware Segmentation: A Novel Framework for MRIs Brain Tumor Segmentation
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“In addition, in the field of classification tasks, the use of conditional output to harness hierarchy has been extensively utilized [43].”
Syaroni, I., & Yen, C.-T. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202564001019
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Machine Unlearning for Pathological Image Classification Models
Kim, M. J., Cho, J. H., Yook, H. J., Kim, Y. S., Kim, S. Y., & Lee, Y. K. (2025) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc66702.2025.11388747
Benchmarking Deep Learning Architectures for Forest Monitoring and Management: A Systematic ReviewPreprint
Forkuo, G. O. (2026) · Research Square · doi:10.21203/rs.3.rs-9088244/v1
Wei, L., Zeng, J., Feng, Y., Liao, X., & Yang, H. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1786674
Analysis and Recommendation Methods for Class-Incremental Learning
Feillet, E. (2024) · theses.fr (ABES) · doi:10.70675/6a2efeadzbf0cz4783z8c67z0d5e86d6cb48
Real-time Indoor Localization with Embedded Computer Vision and Deep Learning
Daou, A. (2024) · theses.fr (ABES) · doi:10.70675/5b59245azb56bz414ez83b3z0125f8f15855
Class-Incremental Learning in an Exemplar-Free setup
Petit, G. (2023) · theses.fr (ABES) · doi:10.70675/ae6798d0za988z4ed5zaaaaz3820057dd603
Shi, Q., Lou, N., & Xue, C. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1769097 · Cited by 1
“AI, particularly DL, is fundamentally characterized by the construction of multi-layer neural networks that autonomously learn hierarchical feature representations from raw data (Roy et al., 2020).”
Thangamani, D., Jayabalan, J., & Nandakumar, E. (2026) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70403
Borovyk, D., Barmak, O., Radiuk, P., & Krak, I. V. (2026) · Technologies · doi:10.3390/technologies14060360
Gu, J., Cai, Q., & Wang, H. (2026) · Journal of Neural Engineering · doi:10.1088/1741-2552/ae805f
“In 2020, Roy et al adopted a hierarchical tree-structured framework composed of convolutional neural networks (Tree-CNN) to mitigate catastrophic forgetting in incremental learning [17].”
Bartusik‐Aebisher, D., Czech, S., Szpara, J., Paul, A., Xavierselvan, M., & Aebisher, D. (2026) · Algorithms · doi:10.3390/a19070524
Deep learning-based computer vision in forest monitoring and management: a systematic review
Forkuo, G. O., & Borz, S. A. (2026) · Biodiversity and Conservation · doi:10.1007/s10531-026-03412-x
“…2020; Oktaria et al. 2019) and generic forest-scene classification (Mardiyah and Purwaningsih 2020), while hierarchical tree-structured learning (Roy et al. 2020 ; Liu et al. 2018 ), asymmetric transfer-learning networks (Shi et al. 2021a , b ), and hyperspectral-specific CNNs (Paoletti et al.…”
Selection of FTIR Characteristic Spectra of Microplastics Based on EIS-CARS and ANN Recognition
时, 嘉. (2026) · Advances in Environmental Protection · doi:10.12677/aep.2026.167123
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
Belouadah, E., Popescu, A., & Kanellos, I. (2020) · arXiv · doi:10.48550/arxiv.2011.01844 · Cited by 235
“Recently, [46] propose an adaptive network that enables self-growth in a tree-like manner.”
Background
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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