4 articles tagged with #plug-and-play. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers have developed GILC, a plug-and-play framework that enables efficient controllable generation in discrete diffusion models without retraining. The method uses gradient-informed logit correction and a Jacobian-free mechanism to stabilize guidance across DNA, protein, and molecular generation tasks, achieving state-of-the-art results.
Researchers introduce PnP-CM, a new method that reformulates consistency models as proximal operators within plug-and-play frameworks for solving inverse problems. The approach achieves high-quality image reconstructions with minimal neural function evaluations (4 NFEs), demonstrating practical efficiency gains over existing consistency model solvers and marking the first application of CMs to MRI data.
EasyLens is a training-free method that enhances medical vision-language models' ability to detect subtle lesions in clinical images without requiring additional model training or adaptation. The approach uses prototype-based reasoning and representation amplification to ensure weak visual cues from lesions aren't lost in global image representations, outperforming existing enhancement methods across multiple medical datasets.
Researchers have developed a gradient-step plug-and-play algorithm that uses a trained denoiser model to reduce photon noise in dental cone-beam CT reconstructions. The method combines inverse problem formulation with machine learning, demonstrating effective denoising on synthetic data and promising generalization to real-world dental imaging applications.
