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Found 5 papers, 2 papers with code
Fast Implicit Neural Representation Image Codec in Resource-limited Devices
With different parameter settings, our method can outperform popular AE-based codecs in constrained environments in terms of both quality and decoding time, or achieve state-of-the-art reconstruction quality compared to other INR codecs.
Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation
EDH mitigates the divergence between labeled data of source subjects and unlabeled data of target subjects to accurately classify the locomotion modes of target subjects without labeling data.
Preserving Domain Private Representation via Mutual Information Maximization
Recent advances in unsupervised domain adaptation have shown that mitigating the domain divergence by extracting the domain-invariant representation could significantly improve the generalization of a model to an unlabeled data domain.
Data-driven Sensor Deployment for Spatiotemporal Field Reconstruction
This paper concerns the data-driven sensor deployment problem in large spatiotemporal fields.
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment
To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution.
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Domain Adaptation
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