no code implementations • 31 May 2024 • Hansang Lee, Haeil Lee, Helen Hong
This process improves the quality and diversity of synthetic data while simultaneously benefiting from the new pattern learning of generative models and the boundary enhancement of mixture models.
no code implementations • 1 Dec 2022 • Hansang Lee, Haeil Lee, Helen Hong, Junmo Kim
Our experiments show that (1) TTMA-DU more effectively differentiates correct and incorrect predictions compared to existing uncertainty measures due to mixup perturbation, and (2) TTMA-CSU provides information on class confusion and class similarity for both datasets.
no code implementations • 1 Dec 2022 • Hansang Lee, Haeil Lee, Helen Hong, Junmo Kim
In the classifier learning, we propose the NoiseMix method based on MixUp and BalancedMix methods by mixing the samples from the noisy and the clean label data.