1 citations · 1 across the 7 of their papers we have counts for
7 papers
Training-free, Perceptually Consistent Low-Resolution Previews with High-Resolution Image for Efficient Workflows of Diffusion Models
Wongi Jeong, Hoigi Seo, Se Young Chun
Image generative models have become indispensable tools to yield exquisite high-resolution (HR) images for everyone, ranging from general users to professional designers. However,…
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…
Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis
Byung Hyun Lee, Wongi Jeong, Woojae Han +2
Multiple instance learning (MIL) significantly reduced annotation costs via bag-level weak labels for large-scale images, such as histopathological whole slide images (WSIs). Howev…
Training-free Mixed-Resolution Latent Upsampling for Spatially Accelerated Diffusion Transformers
Wongi Jeong, Kyungryeol Lee, Hoigi Seo +1
Diffusion transformers (DiTs) offer excellent scalability for high-fidelity generation, but their computational overhead poses a great challenge for practical deployment. Existing…
Efficient Personalization of Quantized Diffusion Model without Backpropagation
Hoigi Seo, Wongi Jeong, Kyungryeol Lee +1
Diffusion models have shown remarkable performance in image synthesis, but they demand extensive computational and memory resources for training, fine-tuning and inference. Althoug…
Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation
Hoigi Seo, Wongi Jeong, Jae-sun Seo +1
Large-scale text encoders in text-to-image (T2I) diffusion models have demonstrated exceptional performance in generating high-quality images from textual prompts. Unlike denoising…
Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts
Dongyoon Han, Junsuk Choe, Seonghyeok Chun +5
Supervised learning of image classifiers distills human knowledge into a parametric model through pairs of images and corresponding labels (X,Y). We argue that this simple and wide…