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20162021
most citedXNOR-Net++: Improved Binary Neural Networks

118 citations · 232 across the 19 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.CV2019★ 1 cited

Towards Pose-invariant Lip-Reading

Shiyang Cheng, Pingchuan Ma, Georgios Tzimiropoulos +4

Lip-reading models have been significantly improved recently thanks to powerful deep learning architectures. However, most works focused on frontal or near frontal views of the mou…

cs.CV2019★ 8 cited

Object landmark discovery through unsupervised adaptation

Enrique Sanchez, Georgios Tzimiropoulos

This paper proposes a method to ease the unsupervised learning of object landmark detectors. Similarly to previous methods, our approach is fully unsupervised in a sense that it do…

cs.CV2019★ 118 cited

XNOR-Net++: Improved Binary Neural Networks

Adrian Bulat, Georgios Tzimiropoulos

This paper proposes an improved training algorithm for binary neural networks in which both weights and activations are binary numbers. A key but fairly overlooked feature of the c…

cs.CV2019

AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

Muhammad Haris Khan, John McDonagh, Salman Khan +5

Being heavily reliant on animals, it is our ethical obligation to improve their well-being by understanding their needs. Several studies show that animal needs are often expressed…

cs.CV2019★ 4 cited

Matrix and tensor decompositions for training binary neural networks

Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos +1

This paper is on improving the training of binary neural networks in which both activations and weights are binary. While prior methods for neural network binarization binarize eac…

cs.CV2019★ 40 cited

Improved training of binary networks for human pose estimation and image recognition

Adrian Bulat, Georgios Tzimiropoulos, Jean Kossaifi +1

Big neural networks trained on large datasets have advanced the state-of-the-art for a large variety of challenging problems, improving performance by a large margin. However, unde…