5 papers
Triadic Dynamics Aware Diffusion Posterior Sampling for Inverse Problems: Optimizing Guidance and Stochasticity Schedules
Junseo Bang, Dong Ju Mun, Hoigi Seo +2
Generative posterior sampling using diffusion models has emerged as a dominant paradigm for solving inverse problems in imaging, which usually consists of three main components: da…
DiffBMP: Differentiable Rendering with Bitmap Primitives
Seongmin Hong, Junghun James Kim, Daehyeop Kim +2
We introduce DiffBMP, a scalable and efficient differentiable rendering engine for a collection of bitmap images. Our work addresses a limitation that traditional differentiable re…
Unlearning the Unpromptable: Prompt-free Instance Unlearning in Diffusion Models
Kyungryeol Lee, Kyeonghyun Lee, Seongmin Hong +2
Machine unlearning aims to remove specific outputs from trained models, often at the concept level, such as forgetting all occurrences of a particular celebrity or filtering conten…
Gradient-free Decoder Inversion in Latent Diffusion Models
Seongmin Hong, Suh Yoon Jeon, Kyeonghyun Lee +2
In latent diffusion models (LDMs), denoising diffusion process efficiently takes place on latent space whose dimension is lower than that of pixel space. Decoder is typically used…
Adaptive Selection of Sampling-Reconstruction in Fourier Compressed Sensing
Seongmin Hong, Jaehyeok Bae, Jongho Lee +1
Compressed sensing (CS) has emerged to overcome the inefficiency of Nyquist sampling. However, traditional optimization-based reconstruction is slow and can not yield an exact imag…