Batch-level Experience Replay with Review for Continual Learning
This work addresses the problem of continual learning for computer vision researchers, but it is incremental as it builds on existing experience replay methods.
The paper tackled the challenge of balancing stability and plasticity in continual learning for computer vision, achieving first place in all three scenarios of the CVPR 2020 CLVision challenge out of 79 teams.
Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity. The CVPR 2020 CLVision Continual Learning for Computer Vision challenge is dedicated to evaluating and advancing the current state-of-the-art continual learning methods using the CORe50 dataset with three different continual learning scenarios. This paper presents our approach, called Batch-level Experience Replay with Review, to this challenge. Our team achieved the 1'st place in all three scenarios out of 79 participated teams. The codebase of our implementation is publicly available at https://github.com/RaptorMai/CVPR20_CLVision_challenge