Exploring Example Influence in Continual Learning

By Qing Sun, Fan Lyu, Fanhua Shang, Wei Feng, Liang Wan. 2022

66 citations recorded by the source corpus.

Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new tasks). Due to the fact that past training data is not available, it is valuable to explore the influence difference on S and P among training examples, which may improve the learning pattern towards better SP. Inspired by Influence Function (IF), we first study example influence via adding perturbation to example weight and computing the influence derivation. To avoid the storage and calculation burden of Hessian inverse in neural networks, we propose a simple yet effective MetaSP algorithm to simulate the two key steps in the computation of IF and obtain the S- and P-aware example influence. Moreover, we propose to fuse two kinds of example influence by solving a dual-objective optimization problem, and obtain a fused influence towards SP Pareto optimality. The fused influence can be used to control the update of model and optimize the storage of rehearsal. Empirical results show that our algorithm significantly outperforms state-of-the-art methods on both task- and class-incremental benchmark CL datasets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning

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Citations in the corpus

A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “MetaSP [397] leverages the Pareto optimum of example influence on stability-plasticity to control the model and storage updates.”
InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning (2024, background): “…of the subspace M ⊥ t in different Transformer layers of ViT-B/16. ﬁnd We can that the dimension of the subspace M ⊥ t in different Transformer layers of ViT-B/16 is always much larger than zero, which means the space for learning the new task always exists throughout the whole learning process.”