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코드

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

https://github.com/HolmesShuan/OISR-PyTorch

Neural Ordinary Differential Equations

https://github.com/HongguangZhang/DMPHN-cvpr19-master

Deep Stacked Hierarchical Multi-patch Network for Image Deblurring

https://github.com/HosseinEbrahimiK/Normalizing-Flows-Reproducing

Neural Ordinary Differential Equations

https://github.com/HosseinEbrahimiK/TrajectoryNet-Reproducing

Neural Ordinary Differential Equations

https://github.com/HritwikEkade/Image-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/HuikaiShao/JPFA

A Joint Pixel and Feature Alignment Framework for Cross-dataset Palmprint Recognition

https://github.com/HungryCookie/VisDrone_CenterNet

Objects as Points

https://github.com/HyeongJu916/Boaz-SR-ESRGAN-PyTorch

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/IAHispano/Applio/tree/exp/vocoders/rvc/lib/algorithm

BigVGAN: A Universal Neural Vocoder with Large-Scale Training

https://github.com/IDEA-Research/detrex/blob/main/projects/h_deformable_detr/README.md

DETRs with Hybrid Matching

https://github.com/IIGROUP/Multi-Modal-CelebA-HQ-Dataset

Conditional Image Generation and Manipulation for User-Specified Content

https://github.com/IIGROUP/TediGAN

ManiGAN: Text-Guided Image Manipulation

https://github.com/IMAC-projects/Deblurring-PyTorch

Scale-recurrent Network for Deep Image Deblurring

https://github.com/IMAC-projects/SRN-Deblurring-PyTorch

Scale-recurrent Network for Deep Image Deblurring

https://github.com/INVOKERer/LoFormer

Intriguing Findings of Frequency Selection for Image Deblurring

https://github.com/IRVLab/funie-gan

Fast Underwater Image Enhancement for Improved Visual Perception

https://github.com/IShengFang/ishengfang

Self-Contained Stylization via Steganography for Reverse and Serial Style Transfer

https://github.com/IVRL/MulT

MulT: An End-to-End Multitask Learning Transformer

https://github.com/Iamnotabear/Neural-Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/IdanAzuri/pytorch_fewshot_with_deep_photo

Deep Photo Style Transfer

https://github.com/Ign0reLee/EMD_Neural_Style_Transfer_with_Tensorflow

A Unified Framework for Generalizable Style Transfer: Style and Content Separation

https://github.com/IjustAi/smartbrush

SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model

https://github.com/IndigoAI/FontGenerator

Attribute2Font: Creating Fonts You Want From Attributes

https://github.com/Insaniauser/ViPNAS_mindspore

ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search

https://github.com/Intelligent-Imaging-Center/Spectral-Reconstruction

MXR-U-Nets for Real Time Hyperspectral Reconstruction

https://github.com/IpastorSan/neural_style_transfer

A Neural Algorithm of Artistic Style

https://github.com/J3698/AdaIN-reimplementation

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

https://github.com/JDAI-CV/DSD-SATN

Human Mesh Recovery from Monocular Images via a Skeleton-disentangled Representation

https://github.com/JDAI-CV/Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving

Down to the Last Detail: Virtual Try-on with Detail Carving

https://github.com/JHKim-snu/GVCCI

OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

https://github.com/JHKim-snu/PGA

OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

https://github.com/JHUVisionLab/multi-modal-regression

A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images

https://github.com/JL321/CycleGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/JSeam2/Neural-Ordinary-Differential-Equations

Neural Ordinary Differential Equations

https://github.com/JaZz-9/Try-First

VITON: An Image-based Virtual Try-on Network

https://github.com/JaZz-9/Try-First-Flask-

VITON: An Image-based Virtual Try-on Network

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