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07/14/2023
PseudoCal: A Source-Free Approach to Unsupervised Uncertainty Calibration in Domain Adaptation
Unsupervised domain adaptation (UDA) has witnessed remarkable advancemen...
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12/16/2021
UMAD: Universal Model Adaptation under Domain and Category Shift
Learning to reject unknown samples (not present in the source classes) i...
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06/09/2021
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
A central challenge in training classification models in the real-world ...
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04/25/2021
How Well Self-Supervised Pre-Training Performs with Streaming Data?
The common self-supervised pre-training practice requires collecting mas...
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04/04/2021
Distill and Fine-tune: Effective Adaptation from a Black-box Source Model
To alleviate the burden of labeling, unsupervised domain adaptation (UDA...
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02/12/2021
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
Contrastive self-supervised learning (CSL) leverages unlabeled data to t...
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12/14/2020
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a r...
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07/08/2020
Combating Domain Shift with Self-Taught Labeling
We present a novel method to combat domain shift when adapting classific...
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03/30/2020
PANDA: Prototypical Unsupervised Domain Adaptation
Previous adversarial domain alignment methods for unsupervised domain ad...
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03/05/2020
A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation
This work addresses the unsupervised domain adaptation problem, especial...
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02/20/2020
