A continual learning survey: Defying forgetting in classification tasks

Abstract

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern 1) a taxonomy and extensive overview of the state-of-the-art, 2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner, 3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods and 4 baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time, and storage.

arXiv commentAccepted TPAMI paper, including Appendix, code publicly available

References in corpus (42)

• Distilling the Knowledge in a Neural Network
• Overcoming catastrophic forgetting in neural networks
• Improving neural networks by preventing co-adaptation of feature detectors
• Fine-Grained Visual Classification of Aircraft
• Progressive Neural Networks
• Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
• Continual Learning Through Synaptic Intelligence
• An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
• PathNet: Evolution Channels Gradient Descent in Super Neural Networks
• A Survey on Multi-Task Learning
• Efficient Lifelong Learning with A-GEM
• Towards Open World Recognition
• Experience Replay for Continual Learning
• On Tiny Episodic Memories in Continual Learning
• New insights and perspectives on the natural gradient method
• Progress & Compress: A scalable framework for continual learning
• Variational Continual Learning
• Overcoming Catastrophic Forgetting by Incremental Moment Matching
• Overcoming catastrophic forgetting with hard attention to the task
• Encoder Based Lifelong Learning
• Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines
• Qualitatively characterizing neural network optimization problems
• Reinforced Continual Learning
• Measuring Catastrophic Forgetting in Neural Networks
• Towards Robust Evaluations of Continual Learning
• Less-forgetting Learning in Deep Neural Networks
• Revisiting Natural Gradient for Deep Networks
• A PAC-Bayesian bound for Lifelong Learning
• Uncertainty-based Continual Learning with Adaptive Regularization
• Lifelong Generative Modeling
• On Quadratic Penalties in Elastic Weight Consolidation
• A comprehensive, application-oriented study of catastrophic forgetting in DNNs
• Open-world Learning and Application to Product Classification
• Gradient based sample selection for online continual learning
• Selfless Sequential Learning
• Task Agnostic Continual Learning Using Online Variational Bayes
• Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks
• A Unifying Bayesian View of Continual Learning
• Toward Understanding Catastrophic Forgetting in Continual Learning
• Improving and Understanding Variational Continual Learning
• Neural Topic Modeling with Continual Lifelong Learning
• Continual Classification Learning Using Generative Models

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• Continual Model-Based Reinforcement Learning with Hypernetworks
• Contrastive Supervised Distillation for Continual Representation Learning
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• Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
• Positive-Congruent Training: Towards Regression-Free Model Updates
• Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts
• Continual Learning Using Bayesian Neural Networks
• Towards Fast and Stable Federated Learning: Confronting Heterogeneity via Knowledge Anchor
• CL2R: Compatible Lifelong Learning Representations
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• Sequoia: A Software Framework to Unify Continual Learning Research
• MEIL-NeRF: Memory-Efficient Incremental Learning of Neural Radiance Fields
• Continual Learning with Fully Probabilistic Models
• VIPeR: Visual Incremental Place Recognition with Adaptive Mining and Continual Learning
• Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector
• Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data
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• Co-Transport for Class-Incremental Learning
• Continual Learning in the Presence of Repetition
• I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning
• Memory-Free Generative Replay For Class-Incremental Learning
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