self-supervised learning
in microscopy imaging Appendix
A.1 Data sources We describe the full license of the original datasets and the URL for download.
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Contrastive-Equivariant Self-Supervised Learning Improves Alignment with Primate Visual Area IT Thomas Y erxa
"contrastive-equivariant" versions that encourage preservation of input transformations without supervised access to the transformation parameters.
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Implicit variance regularization in non-contrastive SSL
In this work, we provide a comparative analysis of the learning dynamics for the Euclidean and cosine-based asymmetric losses in the eigenspace of the closed-form predictor DirectPred.
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Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm, William Buchwalter
Neural Information Processing Systems http://nips.cc/
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