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Found 4 papers, 1 papers with code
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation
Conventional domain adaptation algorithms aim to achieve better generalization by aligning only the task-discriminative causal factors between a source and target domain.
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
We are the first to utilize vision transformers for domain adaptation in a privacy-oriented source-free setting, and our approach achieves state-of-the-art performance on single-source, multi-source, and multi-target benchmarks
Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation
In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL).
Towards Data-Free Model Stealing in a Hard Label Setting
In this work, we show that it is indeed possible to steal Machine Learning models by accessing only top-1 predictions (Hard Label setting) as well, without access to model gradients (Black-Box setting) or even the training dataset (Data-Free setting) within a low query budget.
