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Hi, have you tested the technique mixup (Mixup: Beyond Empirical Risk Minimization) for the different datasets? Is with this technique models can show better performance on dataset IJBC?
The text was updated successfully, but these errors were encountered:
I only tried the vanilla mixup augment method very earlier, and the results not good. I think it's not compatible with margin loss functions like ArcFace:
Mixup will expect a ground truth prediction like, let's say 0.5 mixup, [0, 0, ..., 0.5, ..., 0.5, 0, ...]
ArcFace makes a margin for truth predictions, like 0.8 -> 0.4, 0.5 -> 0.1. This makes them pretty low.
Some basic results using EfficientNetV2B0 + MS1MV3:
Hi, have you tested the technique mixup (Mixup: Beyond Empirical Risk Minimization) for the different datasets? Is with this technique models can show better performance on dataset IJBC?
The text was updated successfully, but these errors were encountered: