DualTTA improves test-time adaptation by adaptively selecting reliable samples for entropy minimization and unreliable samples for entropy maximization based on stability under semantic-preserving and altering transformations.
A brief review of domain adaptation
4 Pith papers cite this work. Polarity classification is still indexing.
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DIN-Retrieval uses domain-invariant neuron representations to retrieve cross-domain demonstrations, achieving an average 1.8-point gain over state-of-the-art methods on mathematical and logical reasoning tasks.
PDA-GAN with pixel discriminator bridges domain gap from inpainted posters to generate SOTA image-aware layouts on a new 60k-pair CGL-Dataset.
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.
citing papers explorer
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Dual Strategies for Test-Time Adaptation
DualTTA improves test-time adaptation by adaptively selecting reliable samples for entropy minimization and unreliable samples for entropy maximization based on stability under semantic-preserving and altering transformations.
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Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval
DIN-Retrieval uses domain-invariant neuron representations to retrieve cross-domain demonstrations, achieving an average 1.8-point gain over state-of-the-art methods on mathematical and logical reasoning tasks.
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GAN-based Domain Adaptation for Image-aware Layout Generation in Advertising Poster Design
PDA-GAN with pixel discriminator bridges domain gap from inpainted posters to generate SOTA image-aware layouts on a new 60k-pair CGL-Dataset.
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Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.