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Found 3 papers, 1 papers with code
Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings
However, in small data regimes, we can not obtain a sufficient number of negative pairs or effectively avoid the over-fitting problem when negatives are not used at all.
Playing Lottery Tickets in Style Transfer Models
(2) Using iterative magnitude pruning, we find the matching subnetworks at 89. 2% sparsity in AdaIN and 73. 7% sparsity in SANet, which demonstrates that style transfer models can play lottery tickets too.
A Unified Framework for Contrastive Learning from a Perspective of Affinity Matrix
In this study, we present a new unified contrastive learning representation framework (named UniCLR) suitable for all the above four kinds of methods from a novel perspective of basic affinity matrix.
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