분류 전체보기345 [2025-1] 정인아 - Image Super-Resolution via Iterative Refinement https://arxiv.org/abs/2104.07636 Image Super-Resolution via Iterative RefinementWe present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models to conditional image generation and performs super-resolution through a stochastic denoising process. Inference starts with parxiv.org Intro문제기존 GAN 기반 super-resolution 모델은 보기에 그럴듯해보이고, 실.. 2025. 2. 1. [2025-1] 임재열- Mamba: Linear-Time Sequence Modeling with Selective State Spaces Mamba는 2024년 Albert Gu와 Tri Dao가 제안한 모델입니다. [Mamba]https://arxiv.org/abs/2312.00752 Mamba: Linear-Time Sequence Modeling with Selective State SpacesFoundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module. Many subquadratic-time architectures such as linear attention, gated conv.. 2025. 2. 1. [2025-1] 유경석 - Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference (LCM) https://arxiv.org/pdf/2310.04378 https://blog.outta.ai/171 [2025-1] Latent Consistency Models : Synthesizing High-Resolution ImagesWith Few-Step Inference논문 링크: 2310.04378 참고 논문 리뷰 블로그 링크: Latent Consistency Models : Synthesizing High-Resolution ImagesWith Few-Step Inference 논문 리뷰 :: LOEWEN Latent Consistency Models : Synthesizing High-Resolution ImagesWith Few-Stblog.outta.ai SummaryStable Dif.. 2025. 2. 1. [2025-1] 주서영 - SRDiff : Single image super-resolution with diffusion probabilistic models SRDiff SRDiff: Single Image Super-Resolution with Diffusion Probabilistic ModelsSingle image super-resolution (SISR) aims to reconstruct high-resolution (HR) images from the given low-resolution (LR) ones, which is an ill-posed problem because one LR image corresponds to multiple HR images. Recently, learning-based SISR methods have garxiv.orgNeurocomputing 2021611회 인용※ 참고SR3 Image Super-Resolut.. 2025. 2. 1. 이전 1 ··· 41 42 43 44 45 46 47 ··· 87 다음
