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This work proposes a novel source-prior-driven selective adaptation method to efficiently fine-tune diffusion models, achieving a favorable trade-off between adaptation-retention and generative capability.
This work proposes a label-free criterion that jointly selects the algorithm and hyperparameters for UDA, and achieves better selection performance than other methods and remains effective across different algorithm pools.
This study finds that a capable adapted model usually exists, but identifying it without target labels is difficult: the validator-selected models leave a large and structural target performance gap to the best available one, with no evaluated validator consistently reliable.
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