Distance Metric Learning with Joint Representation Diversification

Jul 12, 2020



Distance metric learning (DML) is to learn a representation space equipped with a metric, such that examples from the same class are closer than examples from different classes with respect to the metric. The recent success of deep neural networks motivates many DML losses that encourage the intra-class compactness and inter-class separability. However, overemphasizing intra-class compactness may potentially cause the neural networks to filter out information that contributes to discriminating examples from unseen classes, resulting in a less generalizable representation. In contrast, we propose not to penalize intra-class distances explicitly and use a Joint Representation Similarity (JRS) regularizer that focuses on penalizing inter-class distributional similarities in a DML framework. The proposed JRS regularizer diversifies the joint distributions of representations from different classes in multiple neural layers based on cross-covariance operators in Reproducing Kernel Hilbert Space (RKHS). Experiments on three well-known benchmark datasets (Cub-200-2011, Cars-196, and Stanford Online Products) demonstrate the effectiveness of the proposed approach.



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The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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