A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning
arXiv:2607.03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target domains. Existing approaches often fail to achieve an optimal trade-off between robustness and accuracy, as pseudo-labels generated by domain-adapted models tend to introduce classification errors under adversarial attacks. In this work, we propose \textbf{SFT+RL}, a two-stage robust UDA framework that integrates Supervised Fine Tu
The increasing complexity of AI models and their deployment in real-world, often adversarial, environments necessitates robust solutions for domain adaptation and explainability.
This development addresses a critical weakness in AI systems, making them more reliable and trustworthy when deployed in diverse and challenging conditions, accelerating adoption in sensitive sectors.
Existing AI systems will become more adaptable to new data distributions and resilient to adversarial attacks, broadening their real-world applicability without re-training from scratch.
- · AI/ML Research & Development
- · Robotics
- · Autonomous Systems
- · Cybersecurity
- · Systems highly vulnerable to domain shift
- · Manual data annotation services (long-term)
AI models become more robust against adversarial attacks and adaptable to new, unseen data environments through fine-tuning and reinforcement learning.
This improved robustness and adaptability reduces the cost and complexity of deploying AI in diverse real-world scenarios, accelerating AI integration in critical infrastructure.
Enhanced trust in AI systems could lead to wider adoption in high-stakes applications, potentially impacting regulatory frameworks and increasing the demand for foundational AI security.
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Read at arXiv cs.AI