103 citations · 316 across the 27 of their papers we have counts for
6 papers · 1 filter
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…
Unsupervised Domain Adaptation of Black-Box Source Models
Haojian Zhang, Yabin Zhang, Kui Jia +1
Unsupervised domain adaptation (UDA) aims to learn models for a target domain of unlabeled data by transferring knowledge from a labeled source domain. In the traditional UDA setti…
Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation
Yabin Zhang, Bin Deng, Kui Jia +1
Motivated by the problem relatedness between unsupervised domain adaptation (UDA) and semi-supervised learning (SSL), many state-of-the-art UDA methods adopt SSL principles (e.g.,…
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
Yabin Zhang, Bin Deng, Hui Tang +2
In this paper, we study the formalism of unsupervised multi-class domain adaptation (multi-class UDA), which underlies a few recent algorithms whose learning objectives are only mo…