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  1. GO Loss: A Gaussian Distribution-Based Orthogonal Decomposition Loss for Classification.Mengxin Liu, Wenyuan Tao, Xiao Zhang, Yi Chen, Jie Li & Chung-Ming Own - 2019 - Complexity 2019:1-10.
    We present a novel loss function, namely, GO loss, for classification. Most of the existing methods, such as center loss and contrastive loss, dynamically determine the convergence direction of the sample features during the training process. By contrast, GO loss decomposes the convergence direction into two mutually orthogonal components, namely, tangential and radial directions, and conducts optimization on them separately. The two components theoretically affect the interclass separation and the intraclass compactness of the distribution of the sample features, respectively. Thus, (...)
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