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We propose a method, called Label Embedding Network, which can learn labelrepresentation (label embedding) during the training process of deep networks.With the proposed method, the label embedding is adaptively and automaticallylearned through back propagation. The original one-hot represented lossfunction is converted into a new loss function with soft distributions, suchthat the originally unrelated labels have continuous interactions with eachother during the training process. As a result, the trained model can achievesubstantially higher accuracy and with faster convergence speed. Experimentalresults based on competitive tasks demonstrate the effectiveness of theproposed method, and the learned label embedding is reasonable andinterpretable. The proposed method achieves comparable or even better resultsthan the state-of-the-art systems. The source code is available at\url{https://github.com/lancopku/LabelEmb}.

Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks