Sayak Paul
Machine Learning Street Talk (MLST)
Unpacking Data Augmentation in Machine Learning
This chapter investigates the innovations in machine learning surrounding data augmentation and its role in contrastive learning. The speakers discuss the significance of representation learning, the interplay between data augmentation techniques and model performance, and how these concepts relate to human cognition and dreaming. They raise philosophical questions and emphasize the necessity for further research to optimize data augmentation strategies in generative models.
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