151 citations · 422 across the 11 of their papers we have counts for
11 papers
Controlling Character Motions without Observable Driving Source
Weiyuan Li, Bin Dai, Ziyi Zhou +2
How to generate diverse, life-like, and unlimited long head/body sequences without any driving source? We argue that this under-investigated research problem is non-trivial at all,…
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
Liang Xiao, Jiaolong Xu, Dawei Zhao +3
Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentati…
Further Analysis of Outlier Detection with Deep Generative Models
Ziyu Wang, Bin Dai, David Wipf +1
The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applica…
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Bin Dai, Ziyu Wang, David Wipf
In narrow asymptotic settings Gaussian VAE models of continuous data have been shown to possess global optima aligned with ground-truth distributions. Even so, it is well known tha…
Diagnosing and Enhancing VAE Models
Bin Dai, David Wipf
Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood. In particula…
Compressing Neural Networks using the Variational Information Bottleneck
Bin Dai, Chen Zhu, David Wipf
Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture. In this paper we f…