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- Stanford UniversityUS33 papers
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64 papers · 2 filters
Few-shot Image Generation with Elastic Weight Consolidation
Yijun Li, Richard Zhang, Jingwan Lu +1
Few-shot image generation seeks to generate more data of a given domain, with only few available training examples. As it is unreasonable to expect to fully infer the distribution…
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
Zongze Wu, Dani Lischinski, Eli Shechtman
We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first sh…
Time-Travel Rephotography
Xuan Luo, Xuaner Zhang, Paul Yoo +3
Many historical people were only ever captured by old, faded, black and white photos, that are distorted due to the limitations of early cameras and the passage of time. This paper…
LayoutGMN: Neural Graph Matching for Structural Layout Similarity
Akshay Gadi Patil, Manyi Li, Matthew Fisher +2
We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout m…
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach
Xingqian Xu, Zhifei Zhang, Zhaowen Wang +3
Text segmentation is a prerequisite in many real-world text-related tasks, e.g., text style transfer, and scene text removal. However, facing the lack of high-quality datasets and…
Deep Image Compositing
He Zhang, Jianming Zhang, Federico Perazzi +2
Image compositing is a task of combining regions from different images to compose a new image. A common use case is background replacement of portrait images. To obtain high qualit…