DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
Learn how DOME enables test-time adaptation by modeling sample-specific domain variables from sparse supervision, improving model robustness to real-world domain shifts
- Implement DOME using vision-language pre-training to model sample-specific domain variables
- Apply DOME to a test-time adaptation task using sparse supervision
- Evaluate the performance of DOME on a benchmark dataset with multidimensional domain shifts
- Compare the results of DOME with existing test-time adaptation methods
- Use DOME to adapt a pre-trained model to a new test domain with limited labeled data
Researchers and engineers working on test-time adaptation and domain generalization can benefit from DOME's ability to model complex domain shifts, while data scientists and ML engineers can apply DOME to improve model robustness in real-world applications
💡 DOME's ability to model sample-specific domain variables allows for more effective test-time adaptation and improved model robustness to real-world domain shifts
🚀 Introducing DOME: a domain encoder that enables test-time adaptation by modeling sample-specific domain variables from sparse supervision! 📈
Key Takeaways
Learn how DOME enables test-time adaptation by modeling sample-specific domain variables from sparse supervision, improving model robustness to real-world domain shifts
Full Article
Abstract:
arXiv:2606.07646v1 Announce Type: cross Abstract: Test-time adaptation (TTA) aims to align a model to shifting test domains using only unlabeled streaming data. Most existing methods implicitly infer a single global domain distribution, ignoring the multidimensional and sample-specific nature of real-world domain shifts, leading to fragile adaptation. We propose DOME, an effective domain encoder that explicitly models each sample's domain in a zero-shot manner. DOME leverages vision-language pre
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