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Found 2 papers, 1 papers with code
LEAD: Self-Supervised Landmark Estimation by Aligning Distributions of Feature Similarity
Existing works in self-supervised landmark detection are based on learning dense (pixel-level) feature representations from an image, which are further used to learn landmarks in a semi-supervised manner.
Neural Pooling for Graph Neural Networks
Our proposed methods have the ability to handle variable number of nodes in different graphs, and are also invariant to the isomorphic structures of graphs.
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