Inductive Learning
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation
Open-Set Domain Adaptation (OSDA) assumes that a target domain contains unknown classes, which are not discovered in a source domain.
Data driven semi-supervised learning
We obtain low regret and efficient algorithms in the online setting, and generalization guarantees in the distributional setting.
SelecMix: Debiased Learning by Contradicting-pair Sampling
For this, we utilize an auxiliary contrastive model with the popular heuristic that biased features are learned preferentially during training.
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
Pre-trained vision-language models (e.g., CLIP) have shown promising zero-shot