1.5k citations · 2.6k across the 50 of their papers we have counts for
7 papers · 1 filter
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…
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…
Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation
Jean Kossaifi, Antoine Toisoul, Adrian Bulat +3
Training deep neural networks with spatio-temporal (i.e., 3D) or multidimensional convolutions of higher-order is computationally challenging due to millions of unknown parameters…
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…
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…
T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor
Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos +1
Recent findings indicate that over-parametrization, while crucial for successfully training deep neural networks, also introduces large amounts of redundancy. Tensor methods have t…
Incremental multi-domain learning with network latent tensor factorization
Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos +1
The prominence of deep learning, large amount of annotated data and increasingly powerful hardware made it possible to reach remarkable performance for supervised classification ta…