The paper introduces the VODA setting for domain adaptation from scratch using vision-language models and presents TS-DRD, which achieves competitive performance on standard benchmarks without source models.
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DynamicGate MLP enables concurrent learning and inference by separating gating from representation parameters, so that even asynchronous updates produce outputs equivalent to a valid fixed model snapshot.
citing papers explorer
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Rethinking the Need for Source Models: Source-Free Domain Adaptation from Scratch Guided by a Vision-Language Model
The paper introduces the VODA setting for domain adaptation from scratch using vision-language models and presents TS-DRD, which achieves competitive performance on standard benchmarks without source models.
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Learning Inference Concurrency in DynamicGate MLP Structural and Mathematical Justification
DynamicGate MLP enables concurrent learning and inference by separating gating from representation parameters, so that even asynchronous updates produce outputs equivalent to a valid fixed model snapshot.