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3D Architecture

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

https://github.com/rciric/hypercoil

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Differentiable programming for functional connectomics

https://github.com/reach2sbera/ldct_nonlocal

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Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising

https://github.com/ready2drop/DTS

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Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformer

https://github.com/realityengines/local_search

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Exploring the Loss Landscape in Neural Architecture Search

https://github.com/redrock303/revisiting-temporal-alignment-for-video-restoration

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Revisiting Temporal Alignment for Video Restoration

https://github.com/ree1s/idm

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Implicit Diffusion Models for Continuous Super-Resolution

https://github.com/reinhardh/supplement_deep_decoder

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Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks

https://github.com/rem105-210/difffashion

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DiffFashion: Reference-based Fashion Design with Structure-aware Transfer by Diffusion Models

https://github.com/renanrojasg/adv_robust_autoencoder

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Inverting Adversarially Robust Networks for Image Synthesis

https://github.com/rengotj/cobra_denoising

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Non-linear aggregation of filters to improve image denoising

https://github.com/renhaa/semantic-diffusion

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Discovering Interpretable Directions in the Semantic Latent Space of Diffusion Models

https://github.com/renyu2016/Generalized-3D-Manipulation

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Learning Generalizable 3D Manipulation With 10 Demonstrations

https://github.com/researchmm/mm-diffusion

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MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

https://github.com/retinal-research/redot

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OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

https://github.com/revelio-diffusion/revelio

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$\textit{Revelio}$: Interpreting and leveraging semantic information in diffusion models

https://github.com/reyhanehne/cvf-sid_pytorch

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CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image

https://github.com/reysam93/Graph_Deep_Decoder

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An Underparametrized Deep Decoder Architecture for Graph Signals

https://github.com/reysam93/graph_denoising

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Robust graph-filter identification with graph denoising regularization

https://github.com/rf5/transfusion-asr

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TransFusion: Transcribing Speech with Multinomial Diffusion

https://github.com/rfuruta/pixelRL

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PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing

https://github.com/rgsl888/U-Finger-A-Fingerprint-Denosing-Network

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U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting

https://github.com/rhgao/on-demand-learning

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On-Demand Learning for Deep Image Restoration

https://github.com/ricedsp/D-AMP_Toolbox

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Learned D-AMP: Principled Neural Network based Compressive Image Recovery

https://github.com/ricedsp/prDeep

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prDeep: Robust Phase Retrieval with a Flexible Deep Network

https://github.com/rktamplayo/DenoiseSum

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Unsupervised Opinion Summarization with Noising and Denoising

https://github.com/rob-platt/n2n4m

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Noise2Noise Denoising of CRISM Hyperspectral Data

https://github.com/robert-graf/mago-sp

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MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI

https://github.com/robert-graf/readable-conditional-denoising-diffusion

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Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation

https://github.com/robinwangsd/diffusion-with-multi-modal-priors

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Controllable Motion Generation via Diffusion Modal Coupling

https://github.com/roboterax/humanoid-gym

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Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning

https://github.com/rockeycoss/spo

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Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

https://github.com/ronjonxu/naid

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NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset

https://github.com/rosedwayane/aieeg

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IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal

https://github.com/rotot0/tab-ddpm

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TabDDPM: Modelling Tabular Data with Diffusion Models

https://github.com/royerlab/ssi-code

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Image Deconvolution via Noise-Tolerant Self-Supervised Inversion

https://github.com/rshaojimmy/ECCV2020-OSAD

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Open-set Adversarial Defense

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.