FOSTER: Feature Boosting and Compression for Class-Incremental Learning

• URL: http://arxiv.org/abs/2204.04662v1
• Date: Sun, 10 Apr 2022 11:38:33 GMT
• Title: FOSTER: Feature Boosting and Compression for Class-Incremental Learning
• Authors: Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
• Abstract summary: Deep neural networks suffer from catastrophic forgetting when learning new categories. We propose a novel two-stage learning paradigm FOSTER, empowering the model to learn new categories adaptively.
• Score: 52.603520403933985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to learn new concepts continually is necessary in this ever-changing world. However, deep neural networks suffer from catastrophic forgetting when learning new categories. Many works have been proposed to alleviate this phenomenon, whereas most of them either fall into the stability-plasticity dilemma or take too much computation or storage overhead. Inspired by the gradient boosting algorithm to gradually fit the residuals between the target and the current approximation function, we propose a novel two-stage learning paradigm FOSTER, empowering the model to learn new categories adaptively. Specifically, we first dynamically expand new modules to fit the residuals of the target and the original model. Next, we remove redundant parameters and feature dimensions through an effective distillation strategy to maintain the single backbone model. We validate our method FOSTER on CIFAR-100, ImageNet-100/1000 under different settings. Experimental results show that our method achieves state-of-the-art performance.

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