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코드

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

https://github.com/baaivision/muse-pytorch

Muse: Text-To-Image Generation via Masked Generative Transformers

https://github.com/baaivision/vid2vid-zero

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/baizhenmao95/2019-ZTE-Algorithm-Competition

DAC: Data-free Automatic Acceleration of Convolutional Networks

https://github.com/balala8/FastViT_pytorch

FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization

https://github.com/baldesco/neural_style_transfer

A Neural Algorithm of Artistic Style

https://github.com/baodi23/hourglass-facekeypoints-detection

Stacked Hourglass Networks for Human Pose Estimation

https://github.com/baoshengyu/deep-high-resolution-net.pytorch

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/bareeka/ganproject

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

https://github.com/baronefr/neural-style-transfer

A Neural Algorithm of Artistic Style

https://github.com/bastianwandt/RepNet

RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation

https://github.com/bastianwandt/elepose

ElePose: Unsupervised 3D Human Pose Estimation by Predicting Camera Elevation and Learning Normalizing Flows on 2D Poses

https://github.com/bayuudachi/Real-esr-gan

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

https://github.com/bazilas/matconvnet-deepReg

Robust Optimization for Deep Regression

https://github.com/bbc/xcnet

Attention-based Stylisation for Exemplar Image Colourisation

https://github.com/bearpaw/pose-attention

Multi-Context Attention for Human Pose Estimation

https://github.com/bearpaw/pytorch-pose

Stacked Hourglass Networks for Human Pose Estimation

https://github.com/beassssry/U

Mask R-CNN

https://github.com/behnoudshafizadeh/ESPCN-SuperResolution-Tensorflow

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/bekkermans/style_transfer

Style Transfer by Relaxed Optimal Transport and Self-Similarity

https://github.com/benadar293/benadar293.github.io

Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset

https://github.com/benearnthof/fm_boosting

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/benjs/hourglass_networks

Stacked Hourglass Networks for Human Pose Estimation

https://github.com/bennycheung/PyDeepStyle

A Neural Algorithm of Artistic Style

https://github.com/bespontaneous/ffn

UniFormer: Unifying Convolution and Self-attention for Visual Recognition

https://github.com/bespontaneous/mca-pytorch

UniFormer: Unifying Convolution and Self-attention for Visual Recognition

https://github.com/bethgelab/siamese-mask-rcnn

Mask R-CNN

https://github.com/bethgelab/stylize-datasets

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

https://github.com/bharat-b7/IPNet

Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction

https://github.com/bharat-b7/MultiGarmentNetwork

Multi-Garment Net: Learning to Dress 3D People from Images

https://github.com/bhpfelix/Variational-Autoencoder-PyTorch

Deep Feature Consistent Variational Autoencoder

https://github.com/billsun9/automated-style-transfer

Exploring the structure of a real-time, arbitrary neural artistic stylization network

https://github.com/blake58/openpose

Convolutional Pose Machines

https://github.com/bloc97/CrossAttentionControl

Prompt-to-Prompt Image Editing with Cross Attention Control

https://github.com/blueardour/AdelaiDet

DirectPose: Direct End-to-End Multi-Person Pose Estimation

https://github.com/bluedream1121/Key.Net_PyTorch

Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters

https://github.com/bmartacho/BAPose

BAPose: Bottom-Up Pose Estimation with Disentangled Waterfall Representations

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