74 citations · 82 across the 3 of their papers we have counts for
6 papers
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning
Huan Wang, Can Qin, Yue Bai +1
Several recent works [40, 24] observed an interesting phenomenon in neural network pruning: A larger finetuning learning rate can improve the final performance significantly. Unfor…
SuperFront: From Low-resolution to High-resolution Frontal Face Synthesis
Yu Yin, Joseph P. Robinson, Songyao Jiang +3
Advances in face rotation, along with other face-based generative tasks, are more frequent as we advance further in topics of deep learning. Even as impressive milestones are achie…
Neural Pruning via Growing Regularization
Huan Wang, Can Qin, Yulun Zhang +1
Regularization has long been utilized to learn sparsity in deep neural network pruning. However, its role is mainly explored in the small penalty strength regime. In this work, we…
Face Recognition: Too Bias, or Not Too Bias?
Joseph P Robinson, Gennady Livitz, Yann Henon +3
We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gend…
Contradictory Structure Learning for Semi-supervised Domain Adaptation
Can Qin, Lichen Wang, Qianqian Ma +3
Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias…
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
Can Qin, Haoxuan You, Lichen Wang +2
Domain Adaptation (DA) approaches achieved significant improvements in a wide range of machine learning and computer vision tasks (i.e., classification, detection, and segmentation…