DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
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 pretraining to extract dense, continuous representations, parameterizes domains as distributional varia
The proliferation of real-world AI deployments necessitates robust adaptation mechanisms to handle diverse and shifting data distributions, making test-time adaptation a critical research area.
Improving test-time adaptation for AI models enhances their reliability and performance in dynamic, real-world environments, accelerating their safe deployment and reducing the need for costly retraining.
AI models could become significantly more resilient and adaptable to unforeseen domain shifts post-deployment, enabling more stable and versatile autonomous systems.
- · AI developers
- · Robotics companies
- · Autonomous vehicle industry
- · Edge AI providers
- · Companies relying on static AI models
- · Sectors with high retraining costs
AI systems become more robust to environmental variations without explicit human intervention.
This reduces the operational overhead and maintenance costs for AI deployments across various industries.
More reliable AI systems accelerate the adoption of autonomous agents in complex, unstructured environments.
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Read at arXiv cs.AI