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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2 Pith papers cite this work, alongside 18 external citations. Polarity classification is still indexing.
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Unsupervised CNN embedding and clustering of Floquet states recovers known nondispersive wave packet regimes in driven helium without labels.
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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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Unsupervised learning for the systematic identification of nondispersive wave packets in driven helium
Unsupervised CNN embedding and clustering of Floquet states recovers known nondispersive wave packet regimes in driven helium without labels.