Brain-Inspired Continual Learning
Liyuan Wang
School of Life Sciences
Department of Computer Science and Technology
Tsinghua University

Continual / Incremental / Lifelong Learning
New Task / Class, New Instance, New Domain
Catastrophic Forgetting
Stability-Plasticity Trade-off

Continual Learning is Getting Hotter and Hotter

ICLR 2022
Top Keywords

McCloskey et al., 1989; McClelland et al., 1995

(Brain-Inspired) Continual Learning Approaches
Regularization-Based Methods
Selectively Penalize Parameter Changes, Fast-Slow Weights
q Synaptic Consolidation, Synaptic Plasticityq

Replay-Based Methods
Old / Generated Data, Old / Generated Featureq Biological Memory Replay, Complementary Learning SystemqArchitecture-Based Methods
Parameter Isolation, Sub-modules / Sub-networks
q Modularization, Neural Inhibition, Engram Ensembleq

Triple Memory Networks: A Brain-Inspired Framework

Weight Consolidation
Extendable Network
Task-Specific Mask

Liyuan Wang, Bo Lei, Qian Li, Hang Su, Jun Zhu,Yi Zhong. TNNLS, 2021.

Experimental Results
Without accessing to the old data, Triple Memory Networks (TMNs) achieve thestate-of-the-art performance in supervised class-incremental learning.

ORDisCo: Semi-supervised Continual Learning
The incremental data are typically partially-labeled in realistic scenarios.
Representative methods lack the ability to exploit the incremental unlabeled data.

Liyuan Wang, KuoYang, Chongxuan Li, Lanqing Hong, Zhenguo Li, Jun Zhu. CVPR 2021.

Experimental Results
Classification

SMB: Replay of Supervised Memory Buffer
UMB: Replay of Unsupervised Memory Buffer

Conditional Generation

Memory Replay with Compression

Liyuan Wang, Xingxing Zhang, KuoYang, LonghuiYu, Chongxuan Li,Lanqing Hong, Shifeng Zhang, Zhenguo Li,Yi Zhong, Jun Zhu. ICLR 2022.

Memory Replay with Compression
Given a limited storage space, our method can efficiently determine a propercompression quality for incoming data, without repetitive training.

Experimental Results
Large-Scale Class-Incremental Learning

Object Detection for Autonomous Driving

AFEC: Active Forgetting of Negative Transfer
If the old knowledge conflicts with the new task learning, then precisely remembering
the old knowledge will further aggravate the interference.
Biological neural networks can actively forget the conflicting information, throughregulating the learning-triggered synaptic expansion and synaptic convergence.

Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong
Bao, Kaisheng Ma, Jun Zhu,Yi Zhong. NeurIPS, 2021

AFEC: Active Forgetting of Negative Transfer
We introduce a forgetting factor β and replace
the posterior that absorbs all the information of
the old tasks by a weighted product distribution:

The optimal forgetting factor can maximize thelearning of each new task:

Experimental Results

Experimental Results

New Task

Old Task

Summary
Continual learning is complex, but all roads lead to Rome;
Successful biological strategies can provide inspirations for and evolve withcomputational models;
Order is the appearance, compatibility is the goal;
Look to the stars (general theoretical insights) and keep feet on the ground(realistic challenges).

Acknowledgement
School of Life Sciences in Tsinghua:Prof. Yi Zhong, Prof. Qian Li
Dept. of Comp. Sci. & Tech. in Tsinghua:Prof. Jun Zhu, Dr. Chongxuan Li (now at Renmin U), Dr. Xingxing Zhang
Brain-inspire AI Project:
Prof. Kaisheng Ma, Prof. Chenglong Bao, Zhongfan Jia
Huawei Noah's Ark Lab:
Dr. Lanqing Hong, Dr. Zhenguo Li, KuoYang, Mingtian Zhang, LonghuiYu

Thank You!