ARVP: Adversarial Reprogramming Visual Prompts for Class-Incremental Learning
Shuping Liu, Wei Li · 2024
Continual learning of new concepts is essential in artificial intelligence systems, especially in the field of image classification, which necessitates incremental knowledge acquisition without catastrophic forgetting. An inherent problem in current methodologies is the stability-plasticity dilemma or excessive storage overhead. In this paper, we propose a novel method called Adversarial Reprogramming Visual Prompts (ARVP) for Class-Incremental Learning (CIL). ARVP employs an adaptive adversarial reprogramming strategy for visual prompts, strategically downsampling background pixels while preserving discriminative pixels, thus maintaining sample discriminability without compromising quality. Furthermore, a Bop-based formulation and an end-to-end training approach are implemented to enhance the learning efficiency and effectiveness of the model. The optimization of ARVP is divided into two stages, focusing on adding visual prompts and downsampling images. Experimental results on CIFAR-100 and Food-101 benchmarks show that integrating ARVP with existing CIL approaches leads to performance enhancements and using the prompted exemplars can achieve a new state-of-the-art CIL accuracy. Further analysis reveals that ARVP remains effective even with reduced old sample usage.