In AdaMatch, we perform two forward passes, and only one of them is respsonsible for updating the Batch Normalization statistics. This is done to account for distribution shifts in the target dataset. In the other forward pass, we only use the source sample, and the Batch Normalization layers are run in inference mode. Logits for the source samples (weakly and strongly augmented versions) from these two passes are slightly different from one another because of how Batch Normalization layers are run. Final logits for the source samples are computed by linearly interpolating between these two different pairs of logits. This induces a form of consistency regularization. This step is referred to as random logit interpolation.