5 papers
Revisiting Out-of-Distribution Detection in Real-time Object Detection: From Benchmark Pitfalls to a New Mitigation Paradigm
Changshun Wu, Weicheng He, Chih-Hong Cheng +2
Out-of-distribution (OoD) inputs pose a persistent challenge to deep learning models, often triggering overconfident predictions on non-target objects. While prior work has primari…
Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs
Chih-Hong Cheng, Changshun Wu, Xingyu Zhao +2
The deployment of generative AI (GenAI) models raises significant fairness concerns, addressed in this paper through novel characterization and enforcement techniques specific to G…
Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey
Yi Zhang, Zhen Chen, Chih-Hong Cheng +6
Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised et…
LoRA-BAM: Input Filtering for Fine-tuned LLMs via Boxed Abstraction Monitors over LoRA Layers
Changshun Wu, Tianyi Duan, Saddek Bensalem +1
Fine-tuning large language models (LLMs) improves performance on domain-specific tasks but can lead to overfitting, making them unreliable on out-of-distribution (OoD) queries. We…
Instance-Level Safety-Aware Fidelity of Synthetic Data and Its Calibration
Chih-Hong Cheng, Paul Stöckel, Xingyu Zhao
Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable al…