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Found 2 papers, 2 papers with code
LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions
Recent research has shown that it is possible to find interpretable directions in the latent spaces of pre-trained Generative Adversarial Networks (GANs).
[Re] Can gradient clipping mitigate label noise?
Nonetheless, with the help of an additional experiment, our results support the authorsʼ claim that partially Huberised losses perform well on real-world datasets subject to label noise.
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