Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks

17 Nov 2016 Le Hou Chen-Ping Yu Dimitris Samaras

In the context of single-label classification, despite the huge success of deep learning, the commonly used cross-entropy loss function ignores the intricate inter-class relationships that often exist in real-life tasks such as age classification. In this work, we propose to leverage these relationships between classes by training deep nets with the exact squared Earth Mover's Distance (also known as Wasserstein distance) for single-label classification... (read more)

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