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Found 3 papers, 1 papers with code
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization
In this paper, we propose a novel graph invariant learning method based on invariant and variant patterns co-mixup strategy, which is capable of jointly generating mixed multiple environments and capturing invariant patterns from the mixed graph data.
Graph Representation Learning
Out-of-Distribution Generalization
Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN
Due to the limitations of sensors, the transmission medium and the intrinsic properties of ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial resolution.
Iterative Grassmannian Optimization for Robust Image Alignment
t-GRASTA iteratively performs incremental gradient descent constrained to the Grassmann manifold of subspaces in order to simultaneously estimate a decomposition of a collection of images into a low-rank subspace, a sparse part of occlusions and foreground objects, and a transformation such as rotation or translation of the image.
