47 citations · 411 across the 76 of their papers we have counts for
14 papers · 1 filter
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning
Yongyi Su, Yushu Li, Nanqing Liu +4
Test-time adaptation (TTA) updates the model weights during the inference stage using testing data to enhance generalization. However, this practice exposes TTA to adversarial risk…
Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization
Yongyi Su, Xun Xu, Kui Jia
Test-Time Adaptation aims to adapt source domain model to testing data at inference stage with success demonstrated in adapting to unseen corruptions. However, these attempts may f…
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…
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training
Yongyi Su, Xun Xu, Tianrui Li +1
Deploying models on target domain data subject to distribution shift requires adaptation. Test-time training (TTT) emerges as a solution to this adaptation under a realistic scenar…
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…