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1st Workshop on Test-Time Adaptation: Model, Adapt Thyself! (MAT) — 1st Workshop on Test-Time Adaptation: Model, Adapt Thyself! (MAT) documentation Skip to main content Back to top Ctrl + K 1st Workshop on Test-Time Adaptation Home Schedule Papers Home Schedule Papers 1st Workshop on Test-Time Adaptation: Model, Adapt Thyself! (MAT) # CVPR 2024 workshop Deep learning for vision has made progress across tasks, domains, and settings by scaling to deeper models and longer training, first in AlexNet through VGG to ResNet, and now in the era of foundation models. As models have deepened, the set of applications has widened, and there are now countless kinds of data (personal, scientific, …) and deployments (in clouds, on cars, …). Will all these use cases be solved in the limit of more data, parameters, and computation for training? A growing body of work proposes that learning during training alone is not enough, and argues that there is p...
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