64 citations · 68 across the 7 of their papers we have counts for
7 papers
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network
Xiang Wang, Yimin Yang, Zhichang Guo +4
Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perfor…
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network
Xiang Wang, Yimin Yang, Qixiang Pang +3
Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this pape…
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Muthu Chidambaram, Xiang Wang, Chenwei Wu +1
Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels. In recent years, Mixup has become a standard primit…
Understanding Edge-of-Stability Training Dynamics with a Minimalist Example
Xingyu Zhu, Zixuan Wang, Xiang Wang +2
Recently, researchers observed that gradient descent for deep neural networks operates in an ``edge-of-stability'' (EoS) regime: the sharpness (maximum eigenvalue of the Hessian) i…
Plateau in Monotonic Linear Interpolation -- A "Biased" View of Loss Landscape for Deep Networks
Xiang Wang, Annie N. Wang, Mo Zhou +1
Monotonic linear interpolation (MLI) - on the line connecting a random initialization with the minimizer it converges to, the loss and accuracy are monotonic - is a phenomenon that…
Towards Demystifying Representation Learning with Non-contrastive Self-supervision
Xiang Wang, Xinlei Chen, Simon S. Du +1
Non-contrastive methods of self-supervised learning (such as BYOL and SimSiam) learn representations by minimizing the distance between two views of the same image. These approache…
SAGNet:Structure-aware Generative Network for 3D-Shape Modeling
Zhijie Wu, Xiang Wang, Di Lin +3
We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships…