Salient Frequency-Aware Exemplar Compression for Resource-Constrained Online Continual Learning

Abstract

Online Class-Incremental Learning (OCIL) enables a model to learn new classes from a data stream. Since data stream samples are seen only once and the capacity of storage is constrained, OCIL is particularly susceptible to Catastrophic Forgetting (CF). While exemplar replay methods alleviate CF by storing representative samples, the limited capacity of the buffer inhibits capturing the entire old data distribution, leading to CF. In this regard, recent papers suggest image compression for better memory usage. However, existing methods raise two concerns: computational overhead and compression defects. On one hand, computational overhead can limit their applicability in OCIL settings, as models might miss learning opportunities from the current streaming data if computational resources are budgeted and preoccupied with compression. On the other hand, typical compression schemes demanding low computational overhead, such as JPEG, introduce noise detrimental to training. To address these issues, we propose Salient Frequency-aware Exemplar Compression (SFEC), an efficient and effective JPEG-based compression framework. SFEC exploits saliency information in the frequency domain to reduce negative impacts from compression artifacts for learning. Moreover, SFEC employs weighted sampling for exemplar elimination based on the distance between raw and compressed data to mitigate artifacts further. Our experiments employing the baseline OCIL method on benchmark datasets such as CIFAR-100 and Mini-ImageNet demonstrate the superiority of SFEC over previous exemplar compression methods in streaming scenarios.