Does it make sense to apply MixUp augmentation in TTA?

Intuitively, doing so doesn’t make sense to me. If we interpolate a test instance randomly with another random test instance, it might just ends up confusing the model and make prediction worse. But I am not completely sure.

Any intuition / explanation / pointer for learning resource on this ?

@sirgarfield I could be wrong, but the way I think about it is:
Mixup is used in order to improve training by exposing the model to additional data than it would have otherwise had access to – we’re increasing the number of times we can do our backward pass to modify the weights without as much of risk for overfitting.

Like you mentioned, we are looking to understand how well our model can parse the data and by adding mixup on the validation or test – we would be hindering our models ability to interpret the sample. Additionally, at test-time we are evaluating our model and since we are not trying to improve our model at this point (via modifying the weights) - we have no need for the additional samples.