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Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
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In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions. We turn a single unlabeled test sample into a self-supervised learning problem, on which we update the model parameters before making a prediction. This also extends naturally to data in an online stream. Our simple approach leads to improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts.
Forward citations
Cited by 2 Pith papers
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Feed Two Birds with One Scone: Exploiting Function-Space Regularization for Both OOD Robustness and ID Fine-Tuning Performance
A CLIP fine-tuning method that penalizes output differences from the pretrained model and prediction differences under augmentation improves both ID accuracy and OOD robustness in reported experiments.
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T3DM: Test-Time Training-Guided Distribution Shift Modelling for Temporal Knowledge Graph Reasoning
T3DM couples an LSTM-predicted entity-distribution auxiliary task at test time with a GAN-based negative sampler to improve temporal knowledge graph link prediction.
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