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Tied-Augment: Controlling Representation Similarity Improves Data Augmentation

Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi-supervised, self-supervised, and supervised training for vision.

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Cite this as

Emirhan Kurtulus, Zichao Li, Yann Dauphin, Ekin D. Cubuk (2024). Dataset: Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. https://doi.org/10.57702/kihj8cro

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Additional Info

Field	Value
Created	December 3, 2024
Last update	December 3, 2024
Author	Emirhan Kurtulus
More Authors	Zichao Li Yann Dauphin Ekin D. Cubuk
Homepage	https://github.com/ekurtulus/tied-augment/tree/main