1.1k citations · 2.5k across the 39 of their papers we have counts for
7 papers · 1 filter
Meta Architecture Search
Albert Shaw, Wei Wei, Weiyang Liu +2
Neural Architecture Search (NAS) has been quite successful in constructing state-of-the-art models on a variety of tasks. Unfortunately, the computational cost can make it difficul…
Simultaneous Edge Alignment and Learning
Zhiding Yu, Weiyang Liu, Yang Zou +4
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications. Recent advances in representation learning have led to c…
Disjoint Mapping Network for Cross-modal Matching of Voices and Faces
Yandong Wen, Mahmoud Al Ismail, Weiyang Liu +2
We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces. Different from the existing methods,…
Learning towards Minimum Hyperspherical Energy
Weiyang Liu, Rongmei Lin, Zhen Liu +4
Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks re…
Decoupled Networks
Weiyang Liu, Zhen Liu, Zhiding Yu +5
Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations. Inspired by the observation tha…
Iterative Learning with Open-set Noisy Labels
Yisen Wang, Weiyang Liu, Xingjun Ma +4
Large-scale datasets possessing clean label annotations are crucial for training Convolutional Neural Networks (CNNs). However, labeling large-scale data can be very costly and err…
Additive Margin Softmax for Face Verification
Feng Wang, Weiyang Liu, Haijun Liu +1
In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification. In genera…