Spatially Adaptive Computation Time for Residual Networks

P. 1790–1799.

Figurnov M., Collins M., Zhu Y., Zhang L., Huang J., Vetrov D., Salakhutdinov R.

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 problem-agnostic. 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 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.

Keywords: computer vision, deep learning

Publication based on the results of: Разработка комбинированных нейробайесовских методов машинного обучения (2017)

In book

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017)

Curran Associates, Inc., 2017.