In this episode we discuss Fake it till you make it: Learning transferable representations from synthetic ImageNet clones
by Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, Yannis Kalantidis. The paper investigates the ability of synthetic images, generated using Stable Diffusion, to replace real images for training models for ImageNet classification. Using only class names to build the dataset, the study explores the usefulness of synthetic clones of ImageNet for training classification models from scratch. The results show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data for transfer.
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