Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves the state-of-the-art (SOTA) performance. However, existing work is mainly built on a small memory buffer containing a few original data, which cannot fully characterize the old data distribution. In this work, we propose memory replay with data compression to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer. Observing that the trade-off between the quality and quantity of compressed data is highly nontrivial for the efficacy of memory replay, we propose a novel method based on determinantal point processes (DPPs) to efficiently determine an appropriate compression quality for currently-arrived training samples. In this way, using a naive data compression algorithm with a properly selected quality can largely boost recent strong baselines by saving more compressed data in a limited storage space. We extensively validate this across several benchmarks of class-incremental learning and in a realistic scenario of object detection for autonomous driving.

翻译：持续学习需要克服过去的灾难性遗忘。 代表旧培训样本的记忆回放已被证明是一种有效的解决方案,并实现了最新工艺(SOTA)性能。 但是,现有工作主要建立在一个小型的记忆缓冲上,其中含有一些原始数据,无法充分描述旧数据分布。 在这项工作中,我们提议用数据压缩来回放记忆,以减少旧培训样本的存储成本,从而增加其可以存储在记忆缓冲中的数量。 观察压缩数据的质量和数量之间的权衡对于记忆回放的效果来说是高度非技术性的,我们建议一种基于决定点过程的新方法,以便有效地确定当前运来的培训样本的适当压缩质量。 这样,使用一种具有适当选择质量的天真的数据压缩算法,可以通过在有限的存储空间保存更压缩的数据,大大提升近期的强基线。 我们广泛验证了这一点,在几个年级入门学习基准中,并在一个独立驾驶物体探测的现实情景中。