T
Teck Yian Lim
Researcher at University of Illinois at Urbana–Champaign
Publications - 10
Citations - 1817
Teck Yian Lim is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Inpainting & Generative model. The author has an hindex of 5, co-authored 8 publications receiving 1479 citations. Previous affiliations of Teck Yian Lim include Nanyang Technological University.
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Proceedings ArticleDOI
Semantic Image Inpainting with Deep Generative Models
Raymond A. Yeh,Chen Chen,Teck Yian Lim,Alexander G. Schwing,Alexander G. Schwing,Mark Hasegawa-Johnson,Minh N. Do +6 more
TL;DR: A novel method for semantic image inpainting, which generates the missing content by conditioning on the available data, and successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.
Posted Content
Semantic Image Inpainting with Perceptual and Contextual Losses.
TL;DR: A novel method for image inpainting based on a Deep Convolutional Generative Adversarial Network that can successfully predict semantic information in the missing region and achieve pixel-level photorealism, which is impossible by almost all existing methods.
Proceedings ArticleDOI
Time-Frequency Networks for Audio Super-Resolution
TL;DR: Time-Frequency Network (TFNet) is introduced, a deep network that utilizes supervision in both the time and frequency domain and a novel model architecture which allows the two domains to be jointly optimized.
Posted Content
Semantic Image Inpainting with Deep Generative Models
Raymond A. Yeh,Chen Chen,Teck Yian Lim,Alexander G. Schwing,Alexander G. Schwing,Mark Hasegawa-Johnson,Minh N. Do +6 more
TL;DR: In this paper, the closest encoding of the corrupted image in the latent image manifold using context and prior losses is found, which is then passed through the generative model to infer the missing content.
Proceedings ArticleDOI
Image Restoration with Deep Generative Models
TL;DR: This work proposes to design the image prior in a data-driven manner, and learns it using deep generative models, and demonstrates that this learned prior can be applied to many image restoration problems using an unified framework.