TTA (Test-Time Augmentation) is an inference technique that applies multiple augmentations to the test input, runs inference on each, and averages the predictions — effectively ensembling over augmented views of the same input to improve prediction quality.

How Does TTA Work?

• Augment: Apply $K$ augmentations to the test input (e.g., flips, crops, rotations, scales).
• Infer: Run the model on each of the $K$ augmented versions.
• Aggregate: Average (or majority vote) the predictions: $hat{y} = frac{1}{K}sum_k f( ext{Aug}_k(x))$.
• Un-augment: For spatial outputs (segmentation, detection), apply the inverse augmentation before averaging.

Why It Matters

• Free Accuracy: Typically 0.5-1.0% accuracy improvement with no model changes or retraining.
• Cost: $K imes$ inference time — trades compute for accuracy.
• Standard Practice: Routinely used in competitions, medical imaging, and safety-critical applications.

TTA is the inference ensemble — running the model multiple times on augmented versions of the input for more reliable predictions.