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cs.CV2024
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
Asad Aali, Giannis Daras, Brett Levac +3
We provide a framework for solving inverse problems with diffusion models learned from linearly corrupted data. Firstly, we extend the Ambient Diffusion framework to enable trainin…
cs.CV2022★ 1 cited
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning
Ali Lotfi Rezaabad, Sidharth Kumar, Sriram Vishwanath +1
Contrastive self-supervised learning methods learn to map data points such as images into non-parametric representation space without requiring labels. While highly successful, cur…