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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/guanrunwei/rd_map_temporal_spatial_denoising_autoencoder
공식 코드https://github.com/gudaochangsheng/MaskUnet
공식 코드Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability
https://github.com/guhuangai/ddm-public
공식 코드Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation
https://github.com/guhuangai/diffusionedge
공식 코드DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection
https://github.com/guillaumejs2403/ace
공식 코드https://github.com/gulucaptain/dynamictrl
공식 코드https://github.com/gulucaptain/videoassembler
공식 코드MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing
https://github.com/guochengqian/TENet
공식 코드Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
https://github.com/guoqincode/focus-on-your-instruction
공식 코드Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation
https://github.com/guoqiushan/egc
공식 코드EGC: Image Generation and Classification via a Diffusion Energy-Based Model
https://github.com/guoshi28/2stagealign
공식 코드A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift
https://github.com/guoshi28/vjdd
공식 코드Toward Accurate and Temporally Consistent Video Restoration from Raw Data
https://github.com/guoyaoshen/rnst-regularization_by_nst
공식 코드Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data
https://github.com/guyuxuan9/tensorllm
공식 코드TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs
https://github.com/gyukai/i2vc
공식 코드I$^2$VC: A Unified Framework for Intra- & Inter-frame Video Compression
https://github.com/h2o64/slips
공식 코드https://github.com/haha-lisa/Diffstyler
공식 코드DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
https://github.com/haha-lisa/style-a-video
공식 코드Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
https://github.com/hai-chao-zhang/oostraj
공식 코드OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising
https://github.com/haiduo/deepkd
공식 코드DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation Trainer
https://github.com/haihabi/generativecrb
공식 코드https://github.com/hamedhaghighi/lidargrit
공식 코드Taming Transformers for Realistic Lidar Point Cloud Generation
https://github.com/hamidreza-dastmalchi/3dd-tta
공식 코드Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models
https://github.com/hanbyel0105/diff-hmr
공식 코드Generative Approach for Probabilistic Human Mesh Recovery using Diffusion Models
https://github.com/handsomewzy/diffv2iqa
공식 코드https://github.com/hanshounsu/d3rm
공식 코드D3RM: A Discrete Denoising Diffusion Refinement Model for Piano Transcription
https://github.com/hanshuyan/adjointdpm
공식 코드https://github.com/hao1635/ascon
공식 코드ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
https://github.com/hao1635/langmamba
공식 코드LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
https://github.com/hao1635/leda
공식 코드Low-dose CT Denoising with Language-engaged Dual-space Alignment
https://github.com/hao1635/lit-former
공식 코드https://github.com/haochen-rye/hnerv
공식 코드https://github.com/haochen-rye/nerv
공식 코드https://github.com/haochen27/ZS-FLIM-Denoising
공식 코드Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning
https://github.com/haofengl/dragnoise
공식 코드Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagation
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