Abstract:We present a novel loss function, namely, GO loss, for classi cation. 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. e two components theoretically a ect the interclass separation and the intraclass comp… Show more
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