DeepAI AI Chat
Log In Sign Up

Balancing Stability and Plasticity through Advanced Null Space in Continual Learning

07/25/2022
by   Yajing Kong, et al.
The University of Sydney
0

Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well. In this paper, we propose a new continual learning approach, Advanced Null Space (AdNS), to balance the stability and plasticity without storing any old data of previous tasks. Specifically, to obtain better stability, AdNS makes use of low-rank approximation to obtain a novel null space and projects the gradient onto the null space to prevent the interference on the past tasks. To control the generation of the null space, we introduce a non-uniform constraint strength to further reduce forgetting. Furthermore, we present a simple but effective method, intra-task distillation, to improve the performance of the current task. Finally, we theoretically find that null space plays a key role in plasticity and stability, respectively. Experimental results show that the proposed method can achieve better performance compared to state-of-the-art continual learning approaches.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/12/2021

Training Networks in Null Space of Feature Covariance for Continual Learning

In the setting of continual learning, a network is trained on a sequence...
10/15/2021

Towards Better Plasticity-Stability Trade-off in Incremental Learning: A simple Linear Connector

Plasticity-stability dilemma is a main problem for incremental learning,...
09/25/2022

Exploring Example Influence in Continual Learning

Continual Learning (CL) sequentially learns new tasks like human beings,...
06/19/2020

SOLA: Continual Learning with Second-Order Loss Approximation

Neural networks have achieved remarkable success in many cognitive tasks...
02/02/2023

Continual Learning with Scaled Gradient Projection

In neural networks, continual learning results in gradient interference ...
01/28/2021

Self-Attention Meta-Learner for Continual Learning

Continual learning aims to provide intelligent agents capable of learnin...
09/20/2023

Create and Find Flatness: Building Flat Training Spaces in Advance for Continual Learning

Catastrophic forgetting remains a critical challenge in the field of con...