Is Second-order Information Helpful for Large-scale Visual Recognition?

ICCV 2017 Peihua LiJiangtao XieQilong WangWangmeng Zuo

By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from low-level to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate complex boundaries of thousands of classes, adequate exploration of feature distributions is important for realizing full potentials of ConvNets... (read more)

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