Incremental Embedding Learning With Disentangled Representation Translation.

Humans are capable of accumulating knowledge by sequentially learning different tasks, while neural networks fail to achieve this due to catastrophic forgetting problems. Most current incremental learning methods focus more on tackling catastrophic forgetting for traditional classification networks. Notably, however, embedding networks that are basic architectures for many metric learning applications also suffer from this problem. Moreover, the most significant difficulty for continual embedding networks is that the relationships between the latent features and prototypes of previous tasks will be destroyed once new tasks have been learned. Accordingly, we propose a novel incremental method for embedding networks, called the disentangled representation translation (DRT) method, to obtain the discriminative class-disentangled features without reusing any samples of previous tasks and while avoiding the perturbation of task-related information. Next, a mask-guided module is specifically explored to adaptively change or retain the valuable information of latent features. This module enables us to effectively preserve the discriminative yet representative features in the disentangled translation process. In addition, DRT can easily be equipped with a regularization item of incremental learning to further improve performance. We conduct extensive experiments on four popular datasets; as the experimental results clearly demonstrate, our method can effectively alleviate the catastrophic forgetting problem for embedding networks.