44 citations · 86 across the 9 of their papers we have counts for
10 papers · 1 filter
Restricted Generative Projection for One-Class Classification and Anomaly Detection
Feng Xiao, Ruoyu Sun, Jicong Fan
We present a simple framework for one-class classification and anomaly detection. The core idea is to learn a mapping to transform the unknown distribution of training (normal) dat…
Balanced Training for Sparse GANs
Yite Wang, Jing Wu, Naira Hovakimyan +1
Over the past few years, there has been growing interest in developing larger and deeper neural networks, including deep generative models like generative adversarial networks (GAN…
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data
Tiantian Fang, Ruoyu Sun, Alex Schwing
Generative adversarial nets (GANs) have been remarkably successful at learning to sample from distributions specified by a given dataset, particularly if the given dataset is reaso…
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning
Haoxiang Wang, Yite Wang, Ruoyu Sun +1
Model-agnostic meta-learning (MAML) and its variants have become popular approaches for few-shot learning. However, due to the non-convexity of deep neural nets (DNNs) and the bi-l…
Does Momentum Change the Implicit Regularization on Separable Data?
Bohan Wang, Qi Meng, Huishuai Zhang +4
The momentum acceleration technique is widely adopted in many optimization algorithms. However, there is no theoretical answer on how the momentum affects the generalization perfor…
Towards a Better Global Loss Landscape of GANs
Ruoyu Sun, Tiantian Fang, Alex Schwing
Understanding of GAN training is still very limited. One major challenge is its non-convex-non-concave min-max objective, which may lead to sub-optimal local minima. In this work,…