A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks

Abstract

The ability of artificial agents to increment their capabilities when confronted with new data is an open challenge in artificial intelligence. The main challenge faced in such cases is catastrophic forgetting, i.e., the tendency of neural networks to underfit past data when new ones are ingested. A first group of approaches tackles forgetting by increasing deep model capacity to accommodate new knowledge. A second type of approaches fix the deep model size and introduce a mechanism whose objective is to ensure a good compromise between stability and plasticity of the model. While the first type of algorithms were compared thoroughly, this is not the case for methods which exploit a fixed size model. Here, we focus on the latter, place them in a common conceptual and experimental framework and propose the following contributions: (1) define six desirable properties of incremental learning algorithms and analyze them according to these properties, (2) introduce a unified formalization of the class-incremental learning problem, (3) propose a common evaluation framework which is more thorough than existing ones in terms of number of datasets, size of datasets, size of bounded memory and number of incremental states, (4) investigate the usefulness of herding for past exemplars selection, (5) provide experimental evidence that it is possible to obtain competitive performance without the use of knowledge distillation to tackle catastrophic forgetting and (6) facilitate reproducibility by integrating all tested methods in a common open-source repository. The main experimental finding is that none of the existing algorithms achieves the best results in all evaluated settings. Important differences arise notably if a bounded memory of past classes is allowed or not.

Accepted for publication in the Elsevier's Neural Networks journal

References in corpus (6)

• Distilling the Knowledge in a Neural Network
• Towards Understanding Knowledge Distillation
• Continual Learning in Generative Adversarial Nets
• Relational Knowledge Distillation
• Few-Shot Class-Incremental Learning
• Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence

Cited by in corpus (6)

• Replay in Deep Learning: Current Approaches and Missing Biological Elements
• A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification
• CL2R: Compatible Lifelong Learning Representations
• Detecting Unknown DGAs without Context Information
• Memory-Free Generative Replay For Class-Incremental Learning
• Selective Replay Enhances Learning in Online Continual Analogical Reasoning