Batch-level Experience Replay with Review for Continual Learning

Abstract

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

References in corpus (5)

• Overcoming catastrophic forgetting in neural networks
• Continual Learning Through Synaptic Intelligence
• Efficient Lifelong Learning with A-GEM
• On Tiny Episodic Memories in Continual Learning
• Less-forgetting Learning in Deep Neural Networks

Cited by in corpus (5)

• A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays
• New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning
• Continual Learning for Monolingual End-to-End Automatic Speech Recognition
• Learning to Continually Learn Rapidly from Few and Noisy Data
• Online Continual Learning in Image Classification: An Empirical Survey