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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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.
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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.