iCaRL: Incremental Classifier and Representation Learning
Open accessAbstract
A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail.
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The reuse of initial layers is not done in FR [56] and results are suboptimal.
“Unfortunately, the reuse of initial layers, is not done in [56] and results are suboptimal.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
The representation-learning description (p. 4) says iCaRL starts from previously learned network weights and updates them, contrary to the claim that it does not reuse initial layers. The Results (p. 7) also report that iCaRL outperforms the comparison methods, rather than describing its results as suboptimal. (LLM, conf=0.82)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredFR does not need a bounded memory for the update.
“The method does not need a bounded memory for the update.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
If the cited method is iCaRL, the paper says the opposite. Section 2.4 (p. 4) states that its stored exemplars never exceed a memory budget of K, and the representation-learning discussion (p. 4) says those exemplars are used during updates. The copy does not identify a method called FR. (LLM, conf=0.84)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measurediCaRL introduces a bounded memory to enable efficient replay, which is its main difference from LwF.
“The main difference with LwF is the introduction of a bounded memory to enable efficient replay.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
Related Work (p. 6) says iCaRL rehearses with stored exemplars, while Results (p. 7) describes its LwF-like comparator as having no exemplar set. Exemplar Management (p. 4) confirms a fixed exemplar budget. But the paper does not identify bounded-memory replay as the main difference from LwF: Related Work also distinguishes their prediction settings, and the Conclusion (p. 9) names other main components of iCaRL. (LLM, conf=0.85)
Experimental — accuracy not yet measuredFixed-representation based IL algorithms do not update the model but are less dependent on memory than fine-tuning based algorithms.
“We also present results with fixedrepresentation based algorithms [6, 23, 24, 56], which do not update the model but are also less dependent on memory.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
The Results (p. 6) say the fixed-representation method freezes its feature representation and previously learned classifier weights, but trains weights for new classes. Thus it does update part of the model. Related Work (p. 5) discusses fixed-representation methods that need not retain all past training data, but does not establish the claimed memory comparison with fine-tuning methods. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe frozen model in FR is a limitation but also an advantage in that it allows the reuse of initial classifier layers, learned with all images, throughout the entire incremental process.
“The frozen model is a limitation but also an advantage in that it allows the reuse of initial classifier layers, learned with all images, throughout the entire incremental process.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
The Results section (p. 6) says the fixed-representation method freezes its feature representation after the first batch and retains classification weights for classes already processed, training only new-class weights thereafter. This supports reuse of the initial layers, but the paper does not say those layers were learned with all images across the incremental process; it specifies the first batch of classes. (LLM, conf=0.84)
Experimental — accuracy not yet measured
The original definition of herding provides a better approximation of the actual class center compared to a static selection.
“The advantage of the original definition is that it provides a better approximation of the actual class center compared to a static selection.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
Section 2.4 (p. 4) says herding-style iterative selection approximates the class mean and requires fewer samples than random subsampling to achieve high approximation quality. It does not establish that it gives a better approximation than static selection generally; the comparison is specifically with random subsampling. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDBLL excels in efficiently preserving knowledge through selective experience replay without needing complex architectural changes or parameter adjustments.
“Compared to ABLL and RBLL, Data-Based Lifelong Learning (DBLL) excels in efficiently preserving knowledge through selective experience replay [17,18] without needing complex architectural changes or parameter adjustments.”
From GPT-based lifelong learning and ANFIS-driven reply memory ratio prediction for aspect-based sentiment analysis (2025) · cites this paper as [17]
The Conclusion (p. 9) describes prioritized exemplar selection and using those exemplars to avoid catastrophic forgetting, supporting the selective-replay point. But Related Work (p. 6) says iCaRL updates model parameters and also uses distillation; the paper does not support the claim that it preserves knowledge without parameter adjustments. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFSCIL has two main frameworks: one uses a feature extractor with a softmax classifier, and the other involves a feature embedding network and the nearest class mean classifier.
“FSCIL has two main frameworks, as shown in Fig. 6. The first uses a feature extractor with a softmax classifier, while the second involves a feature embedding network and the nearest class mean classifier [22, 42, 43].”
From Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey (2025) · cites this paper as [43]
Section 2.2 (p. 3) contrasts a neural-network classifier with a softmax output layer against iCaRL’s learned feature representation and nearest-mean-of-exemplars classifier. The paper does not establish that these are the two main FSCIL frameworks, and its classifier uses means of selected exemplars rather than full class means. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFor replay methods, memory buffer 200 for CIFAR-10 and 2000 for CIFAR100 and Tiny-ImageNet.
“For the replay methods, we use the memory buffer of 200 for CIFAR-10 and 2000 for CIFAR100 and Tiny-ImageNet as in [56, 6].”
From Open-world continual learning: Unifying novelty detection and continual learning (2025) · cites this paper as [56]
The Experiments section (p. 6) says iCaRL stores up to 2,000 exemplars for iCIFAR-100, supporting that part of the buffer-size claim. The cited paper’s benchmarks are iCIFAR-100 and ImageNet ILSVRC2012; the supplied passages do not support a buffer of 200 for CIFAR-10 or 2,000 for Tiny-ImageNet. (LLM, conf=0.88)
Experimental — accuracy not yet measuredClass IL algorithms provide the most interesting results in recent works from literature.
“We focus on class IL algorithms which provide the most interesting results in recent works from literature [7, 8, 14, 23, 24, 28, 56, 75].”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
FT is heavily affected by catastrophic forgetting if a bounded memory is not available, but becomes an interesting baseline if a memory is available.
“It is heavily affected by catastrophic forgetting if a bounded memory is not available [56] but becomes an interesting baseline if a memory is available [7].”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
One early class IL work [56] is shown to outperform continual learning approaches when tested in a common experimental setting [35].
“one early example of such work [56] is shown to outperform continual learning approaches when tested in a common experimental setting [35].”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
FT NEM exploits the external classifier from [56], has interesting performance, and outperforms FT BAL and IL 2 M.
“FT NEM , which exploits the external classifier from [56] also has interesting performance and outperforms FT BAL and IL 2 M .”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
The original comparison in [56] was biased since iCaRL used memory while their version of FT was implemented without memory.
“However, the original comparison in that paper was biased since iCaRL used memory while their version of FT was implemented without memory.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
Vanilla FT has lower performance than more recent methods but still much better than iCaRL, contrary to the comparison presented in [56].
“Vanilla FT has lower performance than more recent methods but still much better than iCaRL , contrary to the comparison presented in [56].”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
When no memory is allowed for past classes, the experiments reported in Table 6 confirm those presented in [56].
“When no memory is allowed for past classes, the experiments reported in Table 6 confirm those presented in [56].”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [56]
If memory capacity is limited, the sample size of a single knowledge category will gradually decrease with task accumulation, and its impact on the model will gradually decrease.
“If the memory capacity is limited [17], the sample size of a single knowledge category will gradually decrease with the accumulation of tasks, and its impact on the model will gradually decrease.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [17]
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- Authors
- Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H.
- Affiliations
- Institute of Science and Technology Austria (AT)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 5533-5542
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2554863749
- Cited by
- 3,988
- References
- 48
- Citations by year
- 2026: 442 · 2025: 727 · 2024: 777 · 2023: 701 · 2022: 499 · 2021: 435 · 2020: 229 · 2019: 131 · 2018: 44 · 2017: 3
- FWCI
- 50.77
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nvidia (Tesla K40)
- European Commission (2007–2013, FP7 2007-2013, 308036, FP7/2007)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- incremental learning, class-incremental learning, representation learning, data stream learning, classifier training, catastrophic forgetting, deep learning
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