Learning Deep Features for Discriminative Localization

CVPR 2016 Bolei ZhouAditya KhoslaAgata LapedrizaAude OlivaAntonio Torralba

In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels. While this technique was previously proposed as a means for regularizing training, we find that it actually builds a generic localizable deep representation that can be applied to a variety of tasks... (read more)

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