Spatially Adaptive Computation Time for Residual Networks

Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problemagnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency
more » ... tational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.
doi:10.1109/cvpr.2017.194 dblp:conf/cvpr/FigurnovCZZHVS17 fatcat:slbxpjvbw5hwvpyxiwqhhiebpu