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

中文翻译：

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批量级经验回放与持续学习的审查

持续学习是深度学习的一个分支，旨在在学习稳定性和可塑性之间取得平衡。CVPR 2020 CL​​Vision 计算机视觉持续学习挑战赛致力于使用具有三种不同持续学习场景的 CORe50 数据集来评估和推进当前最先进的持续学习方法。本文介绍了我们的方法，称为带审核的批处理级体验回放，以应对这一挑战。我们的团队在 79 个参与的团队中在所有三个场景中都获得了第一名。我们实现的代码库可在 https://github.com/RaptorMai/CVPR20_CLVision_challenge 公开获得