Looking Back on Learned Experiences For Class/task Incremental Learning

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A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “[390], RRL [127] and CF-IL [339], which usually further incorporate knowledge distillation to compensate the lost information in model inversion.”
Task-Free Continual Learning via Online Discrepancy Distance Learning (2022, neutral): “One of the most popular approaches to relieve forgetting is by imposing a regularization loss within the optimization procedure [7, 11, 13, 16, 19, 25, 26, 31, 34, 35, 40, 41, 57], where the network’s parameters which are important to the past tasks are penalized when updating.”
Continual Detection Transformer for Incremental Object Detection (2023, background): “Recent incremental learning methods can be divided into two categories: (i) Knowledge Distillation (KD) tries to preserve the knowledge capture in a previous version of the model by matching logits [27,42], feature maps [12], or other information [19, 40, 46, 49, 52] in the new model.”