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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jp-guo/oscar

공식 코드

OSCAR: One-Step Diffusion Codec for Image Compression Across Multiple Bit-rates

https://github.com/jpzhang1810/ldm-robustness

공식 코드

On the Robustness of Latent Diffusion Models

https://github.com/jquetzalcoatl/LSD-GANs

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Using Deep LSD to build operators in GANs latent space with meaning in real space

https://github.com/jsonBackup/Latent-CLIP-Demo

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Controlling Latent Diffusion Using Latent CLIP

https://github.com/jsshin98/ssgnet

공식 코드

Task-specific Scene Structure Representations

https://github.com/jsvir/vad

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SG-VAD: Stochastic Gates Based Speech Activity Detection

https://github.com/juglab/DecoNoising

공식 코드

Improving Blind Spot Denoising for Microscopy

https://github.com/juglab/DenoiSeg

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DenoiSeg: Joint Denoising and Segmentation

https://github.com/juglab/VoidSeg

공식 코드

Leveraging Self-supervised Denoising for Image Segmentation

https://github.com/juglab/microssim

공식 코드

MicroSSIM: Improved Structural Similarity for Comparing Microscopy Data

https://github.com/juglab/ppn2v

공식 코드

Fully Unsupervised Probabilistic Noise2Void

https://github.com/juliawolleb/anomaly_berdiff

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Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection

https://github.com/junaidmalik09/rwdenoise

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Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising

https://github.com/junhuihe2000/exampleebars

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Adaptive Bayesian Multivariate Spline Knot Inference with Prior Specifications on Model Complexity

https://github.com/junoh-kang/ogdm_edm

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Observation-Guided Diffusion Probabilistic Models

https://github.com/junyuchen245/Active-Contour-Wavelet-Seg

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Feature-Based Image Clustering and Segmentation Using Wavelets

https://github.com/junzhuang-code/lindt

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Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation

https://github.com/justinle4/diffusion

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Generative Modeling with Diffusion

https://github.com/justinpinkney/clip2latent

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clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP

https://github.com/jwen307/quality_uq

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Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems

https://github.com/jychoi118/ilvr_adm

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ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

https://github.com/jychoi118/p2-weighting

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Perception Prioritized Training of Diffusion Models

https://github.com/jychoi118/toward_spatial_unbiased

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Toward Spatially Unbiased Generative Models

https://github.com/jzsherlock4869/denoise-ignet

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Exploring Inter-frequency Guidance of Image for Lightweight Gaussian Denoising

https://github.com/k-radlak/IDCNN

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Deep Learning based Switching Filter for Impulsive Noise Removal in Color Images

https://github.com/kagaminccino/LAVSE

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Lite Audio-Visual Speech Enhancement

https://github.com/kagawa588/diffvsgg

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DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation

https://github.com/kaike-zhang/pld

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Personalized Denoising Implicit Feedback for Robust Recommender System

https://github.com/kaiyangshi-ito/nonconvexag

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Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties

https://github.com/kalmannet/hkf_icassp23

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HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising

https://github.com/kamildeja/analysing_ddgm

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On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models

https://github.com/kangliao929/noise-da

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Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration

https://github.com/kantamasuki/rgdm

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Generative diffusion model with inverse renormalization group flows

https://github.com/karlisfre/diffusion-factorization

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Discrete Denoising Diffusion Approach to Integer Factorization

https://github.com/karlnapf/nystrom-kexpfam

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Efficient and principled score estimation with Nyström kernel exponential families

https://github.com/karlyukang/low-resource-multimodal-pre-training

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Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data

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