A framework to enhance generalization of deep metric learning methods using general discriminative feature learning and class adversarial neural networks

Abstract

Deep Metric Learning (DML) methods automatically extract features from data and learn a non-linear transformation from the input to a semantically embedding space. Many DML methods focused to enhance the discrimination power of the learned metric by proposing novel sampling strategies or loss functions. This approach is very helpful when both the training and test examples are selected from the same set of categories. However, it is less effective in many applications of DML such as image retrieval and person-reidentification. Here, the DML should learn general semantic concepts from observed classes and employ them to rank or identify objects from unseen categories. Neglecting the generalization ability of the learned representation and just emphasizing to learn a more discriminative embedding on the observed classes may lead to the overfitting problem. To address this limitation, we propose a framework to enhance the generalization power of existing DML methods in a Zero-Shot Learning (ZSL) setting by general yet discriminative representation learning and employing a class adversarial neural network. To learn a general representation, we employ feature maps of intermediate layers in a deep neural network and enhance their discrimination power through an attention mechanism. Besides, a class adversarial network is utilized to force the deep model to seek class invariant features. We evaluate our work on widely used machine vision datasets in a ZSL setting. Extensive experimental results confirm that our framework can improve the generalization of existing DML methods, and it consistently outperforms baseline DML algorithms on unseen classes.