Learning Deep Features for Discriminative Localization

Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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. Despite the apparent simplicity of global average pooling,
more » ... are able to achieve 37.1% top-5 error for object localization on ILSVRC 2014, which is remarkably close to the 34.2% top-5 error achieved by a fully supervised CNN approach. We demonstrate that our network is able to localize the discriminative image regions on a variety of tasks despite not being trained for them.
doi:10.1109/cvpr.2016.319 dblp:conf/cvpr/ZhouKLOT16 fatcat:4mmwelc4xbgr5gf4erobt5cmpi