2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…
BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection
Changshun Wu, Weicheng He, Chih-Hong Cheng +2
Out-of-distribution (OoD) detection techniques for deep neural networks (DNNs) become crucial thanks to their filtering of abnormal inputs, especially when DNNs are used in safety-…
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…