GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning
This addresses catastrophic forgetting in continual learning for AI systems, but it is incremental as it builds on existing data augmentation techniques.
The paper tackles catastrophic forgetting in class-incremental learning by proposing GradMix, a gradient-based selective mixup method that improves accuracy by minimizing forgetting, outperforming baselines on real datasets.
In the context of continual learning, acquiring new knowledge while maintaining previous knowledge presents a significant challenge. Existing methods often use experience replay techniques that store a small portion of previous task data for training. In experience replay approaches, data augmentation has emerged as a promising strategy to further improve the model performance by mixing limited previous task data with sufficient current task data. However, we theoretically and empirically analyze that training with mixed samples from random sample pairs may harm the knowledge of previous tasks and cause greater catastrophic forgetting. We then propose GradMix, a robust data augmentation method specifically designed for mitigating catastrophic forgetting in class-incremental learning. GradMix performs gradient-based selective mixup using a class-based criterion that mixes only samples from helpful class pairs and not from detrimental class pairs for reducing catastrophic forgetting. Our experiments on various real datasets show that GradMix outperforms data augmentation baselines in accuracy by minimizing the forgetting of previous knowledge.