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Found 6 papers, 3 papers with code
Understanding the (un)interpretability of natural image distributions using generative models
Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions.
Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation
Many imaging tasks require global information about all pixels in an image.
Stabilizing Adversarial Nets With Prediction Methods
Adversarial neural networks solve many important problems in data science, but are notoriously difficult to train.
Weakly Supervised Learning of Heterogeneous Concepts in Videos
Typical textual descriptions that accompany online videos are 'weak': i. e., they mention the main concepts in the video but not their corresponding spatio-temporal locations.
Biconvex Relaxation for Semidefinite Programming in Computer Vision
We propose a general framework to approximately solve large-scale semidefinite problems (SDPs) at low complexity.
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity
We demonstrate the efficacy of our regularizers on a variety of imaging tasks including compressive image recovery, image restoration, and robust PCA.
