103 citations · 145 across the 8 of their papers we have counts for
8 papers
Universal Domain Adaptation from Foundation Models: A Baseline Study
Bin Deng, Kui Jia
Foundation models (e.g., CLIP or DINOv2) have shown their impressive learning and transfer capabilities in a wide range of visual tasks, by training on a large corpus of data and a…
Adversarial Style Augmentation for Domain Generalization
Yabin Zhang, Bin Deng, Ruihuang Li +2
It is well-known that the performance of well-trained deep neural networks may degrade significantly when they are applied to data with even slightly shifted distributions. Recent…
Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization
Bin Deng, Kui Jia
Learning invariant (causal) features for out-of-distribution (OOD) generalization has attracted extensive attention recently, and among the proposals invariant risk minimization (I…
Gradual Domain Adaptation via Self-Training of Auxiliary Models
Yabin Zhang, Bin Deng, Kui Jia +1
Domain adaptation becomes more challenging with increasing gaps between source and target domains. Motivated from an empirical analysis on the reliability of labeled source data fo…
Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners
Yabin Zhang, Haojian Zhang, Bin Deng +3
Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are two typical strategies to reduce expensive manual annotations in machine learning. In order to learn eff…
On Universal Black-Box Domain Adaptation
Bin Deng, Yabin Zhang, Hui Tang +2
In this paper, we study an arguably least restrictive setting of domain adaptation in a sense of practical deployment, where only the interface of source model is available to the…