IL2M: Class Incremental Learning With Dual Memory
Open accessAbstract
This paper presents a class incremental learning (IL) method which exploits fine tuning and a dual memory to reduce the negative effect of catastrophic forgetting in image recognition. First, we simplify the current fine tuning based approaches which use a combination of classification and distillation losses to compensate for the limited availability of past data. We find that the distillation term actually hurts performance when a memory is allowed. Then, we modify the usual class IL memory component. Similar to existing works, a first memory stores exemplar images of past classes. A second memory is introduced here to store past class statistics obtained when they were initially learned. The intuition here is that classes are best modeled when all their data are available and that their initial statistics are useful across different incremental states. A prediction bias towards newly learned classes appears during inference because the dataset is imbalanced in their favor. The challenge is to make predictions of new and past classes more comparable. To do this, scores of past classes are rectified by leveraging contents from both memories. The method has negligible added cost, both in terms of memory and of inference complexity. Experiments with three large public datasets show that the proposed approach is more effective than a range of competitive state-of-the-art methods.
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FT BAL is tributary to the fixed memory K because past exemplars are needed for the balancing step.
“FT BAL is also tributary to the fixed memory K because past exemplars are needed for the balancing step.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [7]
Class 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 [7]
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 [7]
FT NEM can only be deployed if a bounded memory is allowed to alleviate catastrophic forgetting.
“FT NEM can only be deployed if a bounded memory is allowed to alleviate catastrophic forgetting.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [7]
IL2M is prone to catastrophic forgetting if no memory is allowed.
“This approach is also prone to catastrophic forgetting if no memory is allowed.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [7]
The model is biased towards new classes, leading it to predict past images as belonging to new classes.
“This is explained in [7, 8, 75] by the fact that the model is biased towards new classes, leading to predict past images as belonging to new classes.”
From A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020) · cites this paper as [7]
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Continual Learning by Using Information of Each Class Holistically
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Incremental SAR Automatic Target Recognition With Error Correction and High Plasticity
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Few-Shot Class Incremental Learning Leveraging Self-Supervised Features
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Computationally Budgeted Continual Learning: What Does Matter?
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Energy-based Latent Aligner for Incremental Learning
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Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection
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Learnable Distribution Calibration for Few-Shot Class-Incremental Learning
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Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer
Ashok, A., Joseph, K. J., & Balasubramanian, V. N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_7 · Cited by 38 · see citing works
Class-incremental Learning with Pre-allocated Fixed Classifiers
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Online Hyperparameter Optimization for Class-Incremental Learning
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CRNet: A Fast Continual Learning Framework With Random Theory
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Novel Class Discovery Without Forgetting
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Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning
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Generalized Class Incremental Learning
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Adaptive Orthogonal Projection for Batch and Online Continual Learning
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Domain-incremental learning for fire detection in space-air-ground integrated observation network
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Continual Learning for Image Segmentation With Dynamic Query
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Analogical Learning-Based Few-Shot Class-Incremental Learning
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Generative negative replay for continual learning
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Self-Supervised Training Enhances Online Continual LearningPreprint
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Residual Tuning: Toward Novel Category Discovery Without Labels
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Multi-Granularity Regularized Re-Balancing for Class Incremental Learning
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CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
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I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
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Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
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eTag: Class-Incremental Learning via Embedding Distillation and Task-Oriented Generation
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Preserving Earlier Knowledge in Continual Learning with the Help of All Previous Feature ExtractorsPreprint
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Language-Inspired Relation Transfer for Few-Shot Class-Incremental Learning
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IIRC: Incremental Implicitly-Refined Classification
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Balanced Softmax Cross-Entropy for Incremental Learning
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Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning
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Coordinating Experience Replay: A Harmonious Experience Retention approach for Continual Learning
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Online Continual Learning Via Candidates Voting
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- Authors
- Belouadah, E., & Popescu, A.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 583-592
- OpenAlex
- W2981864462
- Cited by
- 302
- References
- 30
- Citations by year
- 2026: 20 · 2025: 44 · 2024: 60 · 2023: 57 · 2022: 47 · 2021: 52 · 2020: 21 · 2019: 1
- FWCI
- 11.57
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- HAL (submittedVersion, cc-by-nc-nd)
- 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 › Machine Learning and Data Classification
- Keywords
- class-incremental learning, catastrophic forgetting, fine-tuning, knowledge distillation, exemplar memory, image recognition, prediction bias, class imbalance
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