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Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
Large-scale pretrained models are widely leveraged as foundations for learning new specialized tasks via fine-tuning, with the goal of maintaining the general performance of the model while allowing it to gain new skills. A valuable goal for all such models is robustness: the ability to perform well
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