This paper explores the theoretical aspects of Test-Time Augmentation (TTA), a heuristic technique that leverages data augmentation during testing to improve model performance. The authors aim to provide theoretical guarantees for TTA and clarify its behavior. Key contributions include: 1. **Theoretical Guarantees**: Proving that the expected error of TTA is less than or equal to the average error of the original model, and under certain assumptions, strictly less. 2. **Generalized TTA**: Introducing a generalized version of TTA with optimal weights derived in closed form. 3. **Error Decomposition**: Show that the error of TTA depends on the ambiguity term, which measures the discrepancy between individual hypotheses. 4. **Statistical Consistency**: Demonstrating that the empirical risk minimization with data augmentations is a consistent estimator of the expected risk. The paper also discusses related work and future directions, emphasizing the need for further theoretical analysis of TTA variants and their performance on different datasets.This paper explores the theoretical aspects of Test-Time Augmentation (TTA), a heuristic technique that leverages data augmentation during testing to improve model performance. The authors aim to provide theoretical guarantees for TTA and clarify its behavior. Key contributions include: 1. **Theoretical Guarantees**: Proving that the expected error of TTA is less than or equal to the average error of the original model, and under certain assumptions, strictly less. 2. **Generalized TTA**: Introducing a generalized version of TTA with optimal weights derived in closed form. 3. **Error Decomposition**: Show that the error of TTA depends on the ambiguity term, which measures the discrepancy between individual hypotheses. 4. **Statistical Consistency**: Demonstrating that the empirical risk minimization with data augmentations is a consistent estimator of the expected risk. The paper also discusses related work and future directions, emphasizing the need for further theoretical analysis of TTA variants and their performance on different datasets.