Constructing accurate training tuples is crucial for unsupervised local descriptor learning, yet challenging due to the absence of patch labels. The state-of-the-art approach constructs with heuristic rules, which struggle precisely depict real-world transformations, in spite enabling fast model convergence. A possible solution alleviate problem clustering-based approach, can capture realistic ...

This letter proposes the unsupervised training of a feedforward neural network to solve parametric optimization problems involving large numbers parameters. Such training, which consists in repeatedly sampling parameter values and performing stochastic gradient descent, foregoes taxing precomputation labeled data that supervised learning necessitates. As an example application, we put this tech...

In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores complementary information between structural neighbor enlarge inter-class variation, as well adds an upper bound achieve finite distance positive embeddings anchor for reducing intra-class variation. As r...