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Found 4 papers, 4 papers with code
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
Real-world datasets exhibit imbalances of varying types and degrees.
Ranked #1 on
Long-tail Learning
on CIFAR-10-LT (ρ=50)
A Closer Look at Smoothness in Domain Adversarial Training
Based on the analysis, we introduce the Smooth Domain Adversarial Training (SDAT) procedure, which effectively enhances the performance of existing domain adversarial methods for both classification and object detection tasks.
Ranked #5 on
Domain Adaptation
on VisDA2017
S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain.
S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain.
