Rethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective
arXiv:2602.01179v2 Announce Type: replace Abstract: Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via intermediate domains. However, real intermediate domains are often unavailable or ineffective, necessitating the synthesis of intermediate samples. Flow-based models have recently been used for this purpose by interpolating between source and target distributions. Notably, their training typically relies on sample-based log-likelihood estimation, which can discard useful information and thus degrade GD
This paper rethinks existing flow-based gradual domain adaptation techniques, addressing limitations in synthesizing intermediate samples for AI model training, suggesting a potential improvement in handling domain shifts.
Improving domain adaptation techniques is crucial for AI model robustness and generalizability, enabling more effective deployment of models in varied real-world environments without extensive retraining.
The proposed 'semi-dual optimal transport perspective' could lead to more efficient and accurate methods for generating synthetic intermediate domains, thereby enhancing the performance of AI systems in diverse applications.
- · AI researchers
- · Machine learning practitioners
- · Industries relying on AI deployment (e.g., healthcare, autonomous vehicles)
- · Methods relying solely on sample-based log-likelihood estimation
- · Less robust domain adaptation techniques
More robust AI models that perform better across different operational environments without requiring large amounts of new data for adaptation.
Reduced development costs and faster deployment cycles for AI applications as models become more easily adaptable to new conditions.
Acceleration of AI integration into areas with significant domain variability, potentially expanding the market for intelligent autonomous systems.
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Read at arXiv cs.LG