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코드

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

https://github.com/ZhaoHengrun/EDVR

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/ZhaojieL/HTE-data

Decoupling Speaker-Independent Emotions for Voice Conversion Via Source-Filter Networks

https://github.com/ZhixinLai/3D-detection-with-monocular-RGB-image

3D Bounding Box Estimation Using Deep Learning and Geometry

https://github.com/ZichengDuan/TheChosenOne

The Chosen One: Consistent Characters in Text-to-Image Diffusion Models

https://github.com/ZiqiaoPeng/EmoTalk

EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation

https://github.com/ZyoungXu/MoSt-DSA

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/a2824256/singleshotpose_imp

Real-Time Seamless Single Shot 6D Object Pose Prediction

https://github.com/aadhithya/AdaIN-pytorch

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

https://github.com/aba450/Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/abaietto/neural_ode_classification

Neural Ordinary Differential Equations

https://github.com/abdulwaheedsoudagar/SR-CNN

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/abhi1kumar/M3D-RPN

M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

https://github.com/abhi1kumar/hrnet_pose_single_gpu

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/abhijeetmittal/Convolutional-Neural-Network

A Neural Algorithm of Artistic Style

https://github.com/abhishtagatya/paintgan

A Neural Algorithm of Artistic Style

https://github.com/abhiskk/fast-neural-style

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/aboev/arae-tf

Adversarially Regularized Autoencoders

https://github.com/acetylSv/GST-tacotron

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

https://github.com/acht7111020/DSMAP

Domain-Specific Mappings for Generative Adversarial Style Transfer

https://github.com/adbobes/VideoSuperResolution

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/adieyal/sd-dynamic-prompts

Optimizing Prompts for Text-to-Image Generation

https://github.com/adipandas/torch_shnet

Stacked Hourglass Networks for Human Pose Estimation

https://github.com/adldotori/ClothFlow

https://github.com/adnanalam53/cycleGAN

Learning from Simulated and Unsupervised Images through Adversarial Training

https://github.com/adobe-research/MakeItTalk

MakeItTalk: Speaker-Aware Talking-Head Animation

https://github.com/aelnouby/Text-to-Image-Synthesis

Generative Adversarial Text to Image Synthesis

https://github.com/afruehstueck/tileGAN

TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

https://github.com/ag17sep/Image-Captioning-Project

Show and Tell: A Neural Image Caption Generator

https://github.com/ag1988/dss

It's Raw! Audio Generation with State-Space Models

https://github.com/agnJason/RGB-D-PIFuHD

PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization

https://github.com/agnesdeng/mixgb

XGBoost: A Scalable Tree Boosting System

https://github.com/ahmedbesbes/cycle-gan

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

https://github.com/ahmedosman/STAR

VIBE: Video Inference for Human Body Pose and Shape Estimation

https://github.com/ahmedoumar/Neural_style_transfer_keras

A Neural Algorithm of Artistic Style

https://github.com/ahmedsalahacc/Image-Style-Transfer-with-Cycle-GAN

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

https://github.com/ai-forever/kandinskyvideo

Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

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