Web: http://arxiv.org/abs/2209.07383

Sept. 16, 2022, 1:15 a.m. | Wenguan Wang, Cheng Han, Tianfei Zhou, Dongfang Liu

cs.CV updates on arXiv.org arxiv.org

We devise deep nearest centroids (DNC), a conceptually elegant yet
surprisingly effective network for large-scale visual recognition, by
revisiting Nearest Centroids, one of the most classic and simple classifiers.
Current deep models learn the classifier in a fully parametric manner, ignoring
the latent data structure and lacking simplicity and explainability. DNC
instead conducts nonparametric, case-based reasoning; it utilizes sub-centroids
of training samples to describe class distributions and clearly explains the
classification as the proximity of test data and the class …


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