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Found 5 papers, 1 papers with code
Meta Variance Transfer: Learning to Augment from the Others
Humans have the ability to robustly recognize objects with various factors of variations such as nonrigid transformation, background noise, and change in lighting conditions.
Rethinking Feature-Based Knowledge Distillation for Face Recognition
By constraining the teacher's search space with reverse distillation, we narrow the intrinsic gap and unleash the potential of feature-only distillation.
Sample-wise Label Confidence Incorporation for Learning with Noisy Labels
Although recent studies on designing a robust objective function to label noise, known as the robust loss method, have shown promising results for learning with noisy labels, they suffer from the issue of underfitting not only noisy samples but also clean ones, leading to suboptimal model performance.
Quality-Agnostic Image Recognition via Invertible Decoder
Our two-stage scheme allows the network to produce clean-like and robust features from any quality images, by reconstructing their clean images via the invertible decoder.
Ranked #17 on
Domain Generalization
on ImageNet-C
DiscFace: Minimum Discrepancy Learning for Deep Face Recognition
Softmax-based learning methods have shown state-of-the-art performances on large-scale face recognition tasks.
Ranked #1 on
Face Verification
on CALFW
