Understanding image representations by measuring their equivariance and equivalence

CVPR 2015 Karel LencAndrea Vedaldi

Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited. Aiming at filling this gap, we investigate three key mathematical properties of representations: equivariance, invariance, and equivalence... (read more)

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