41 citations · 69 across the 10 of their papers we have counts for
9 papers · 1 filter
CheXGenBench: A Unified Benchmark For Fidelity, Privacy and Utility of Synthetic Chest Radiographs
Raman Dutt, Pedro Sanchez, Yongchen Yao +3
Structured benchmarks have advanced text-conditional image generation for real-world imagery, however, no such benchmark exists for synthetic radiograph generation. Despite being a…
Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Models
Raman Dutt, Pedro Sanchez, Ondrej Bohdal +2
In this work, we present compelling evidence that controlling model capacity during fine-tuning can effectively mitigate memorization in diffusion models. Specifically, we demonstr…
MemControl: Mitigating Memorization in Diffusion Models via Automated Parameter Selection
Raman Dutt, Ondrej Bohdal, Pedro Sanchez +2
Diffusion models excel in generating images that closely resemble their training data but are also susceptible to data memorization, raising privacy, ethical, and legal concerns, p…
Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models
Konstantinos Vilouras, Pedro Sanchez, Alison Q. O'Neil +1
Localizing the exact pathological regions in a given medical scan is an important imaging problem that traditionally requires a large amount of bounding box ground truth annotation…
Benchmarking Counterfactual Image Generation
Thomas Melistas, Nikos Spyrou, Nefeli Gkouti +5
Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in…
RadEdit: stress-testing biomedical vision models via diffusion image editing
Fernando Pérez-García, Sam Bond-Taylor, Pedro P. Sanchez +11
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. Thi…