301 citations · 1k across the 20 of their papers we have counts for
4 papers · 1 filter
Partial Convolution based Padding
Guilin Liu, Kevin J. Shih, Ting-Chun Wang +5
In this paper, we present a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks. We call it partial convolution base…
Video-to-Video Synthesis
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu +4
We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an outpu…
Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation
Aysegul Dundar, Ming-Yu Liu, Ting-Chun Wang +2
Deep neural networks have largely failed to effectively utilize synthetic data when applied to real images due to the covariate shift problem. In this paper, we show that by applyi…
Image Inpainting for Irregular Holes Using Partial Convolutions
Guilin Liu, Fitsum A. Reda, Kevin J. Shih +3
Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid…