Lecture
This lecture covers various data augmentation techniques such as standard transforms, DeepAugment, stylization, domain randomization, 3D corruptions, AutoAugment, and AugMix. It also explores ensembling methods including deep ensembles and cross-domain ensembles, comparing ViT and ResNet models for image classification, and test-time adaptation strategies.
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