Proposes FFT-based convolution acceleration, achieving over 10x speedup in training and inference for deep CNNs.

Michael Mathieu, Mikael Henaff, Yann LeCun

DeCAF features extracted from ImageNet-trained CNN outperform traditional features on multiple vision tasks, achieving over 20% accuracy improvements.

Jeff Donahue, Yangqing Jia, Oriol Vinyals et al.

Introduces an efficient inference algorithm for fully connected CRFs, enhancing image segmentation accuracy.

Philipp Krähenbühl, Vladlen Koltun