Search Results for author:
Found 4 papers, 3 papers with code
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image.
Ranked #3 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(using extra training data)
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow.
Ranked #5 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(using extra training data)
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
Investigation of Factorized Optical Flows as Mid-Level Representations
In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks.
