TRICL: Triplet Continual Learning

A class-incremental learning agent learns online with a neverending stream of data in one training epoch. In this setting, the agent suffers from severe catastrophic forgetting due to the absence of data from the observed classes after learning data from new classes. Besides, the prototypes rapidly become outdated as the agent adapts to new data sequentially, and the previous example embeddings spread out in an unforeseen way, which exacerbates forgetting (i.e., concept drift). Based on this observation, we propose a replay-based method, called TriCL, which gathers the embeddings near the prototype from the same class and separates the embeddings from the different class prototypes. TriCL leverages an improved triplet loss without extra arranged input data triplets. To facilitate rapid convergence between the same class samples, we design a memory update algorithm for decreasing the variance among the buffered samples from the same class. Furthermore, we make a prototype compensation strategy for preventing drift. Compared to the state-of-the-art benchmarks, the experiments demonstrate that our proposed method presents improved performance in the online class-incremental learning setting.