Dynamic Y-KD: A Hybrid Approach to Continual Instance Segmentation

TL;DR

This paper introduces Dynamic Y-KD, a novel hybrid approach combining shared feature extractors, task-specific modules, and checkpoint averaging to improve continual instance segmentation and mitigate catastrophic forgetting.

Contribution

It proposes the Y-knowledge distillation technique with a dynamic architecture and checkpoint averaging, advancing continual learning for instance segmentation.

Findings

Outperforms previous methods on old and new classes

Achieves +2.1% mAP on old classes in 15-1 scenario

Reaches 91.5% of joint-training mAP in 15-5 scenario

Abstract

Despite the success of deep learning models on instance segmentation, current methods still suffer from catastrophic forgetting in continual learning scenarios. In this paper, our contributions for continual instance segmentation are threefold. First, we propose the Y-knowledge distillation (Y-KD), a technique that shares a common feature extractor between the teacher and student networks. As the teacher is also updated with new data in Y-KD, the increased plasticity results in new modules that are specialized on new classes. Second, our Y-KD approach is supported by a dynamic architecture method that trains task-specific modules with a unique instance segmentation head, thereby significantly reducing forgetting. Third, we complete our approach by leveraging checkpoint averaging as a simple method to manually balance the trade-off between performance on the various sets of classes, thus increasing control over the model's behavior without any additional cost. These contributions are united in our model that we name the Dynamic Y-KD network. We perform extensive experiments on several single-step and multi-steps incremental learning scenarios, and we show that our approach outperforms previous methods both on past and new classes. For instance, compared to recent work, our method obtains +2.1% mAP on old classes in 15-1, +7.6% mAP on new classes in 19-1 and reaches 91.5% of the mAP obtained by joint-training on all classes in 15-5.