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평가표

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보존된 평가 기록

1

코드

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

https://github.com/Tiannia/intro_to_ai

A Neural Algorithm of Artistic Style

https://github.com/TianxiangMa/MUST-GAN

MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation

https://github.com/TilakD/Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/TomokiKomiya/SRGAN-keras

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

https://github.com/Tonyhuiii/color-transform

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/Trustworthy-AI-Group/TransferAttack

Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer

https://github.com/TuSimple/mx-maskrcnn

Mask R-CNN

https://github.com/Tudouu/stgcn_light_op

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/TureganoJose/ARCproject

Real-time deep hair matting on mobile devices

https://github.com/UBC-Computer-Vision-Group/DwNet

DensePose: Dense Human Pose Estimation In The Wild

https://github.com/UMich-BipedLab/AprilTag_ROS

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/ROB101-ExtrinsicCalibrationProblem

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/automatic_lidar_camera_calibration

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/lidar_simulator

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UMich-BipedLab/sync_lidartag_apriltag

Improvements to Target-Based 3D LiDAR to Camera Calibration

https://github.com/UPCLJ/py-faster-rcnn

Mask R-CNN

https://github.com/UdbhavPrasad072300/Generate-Fake-Faces-with-CVAE-in-PyTorch

Deep Feature Consistent Variational Autoencoder

https://github.com/Uemuet/style-transfer

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/Uemuet/styletransfer-adaptive

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/UofTrees/ProjectX2020

Neural Ordinary Differential Equations

https://github.com/VAShibaev/text_style_transfer

Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites

https://github.com/VCL3D/DronePose

DronePose: Photorealistic UAV-Assistant Dataset Synthesis for 3D Pose Estimation via a Smooth Silhouette Loss

https://github.com/VCL3D/UAVA

DronePose: Photorealistic UAV-Assistant Dataset Synthesis for 3D Pose Estimation via a Smooth Silhouette Loss

https://github.com/VCL3D/VolumetricCapture

Deep Soft Procrustes for Markerless Volumetric Sensor Alignment

https://github.com/VL97/Pose-estimation-using-Deep-CNN-encoder

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/VNOpenAI/tf-blazepose

BlazePose: On-device Real-time Body Pose tracking

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

A Neural Algorithm of Artistic Style

https://github.com/Vaibhavck/Style-Transfer-VGG19

A Neural Algorithm of Artistic Style

https://github.com/Varun-Tandon14/Implementation-of-Cross-View-Tracking-for-Multi-Human-3D-Pose-Estimation-at-over-100-FPS

Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS

https://github.com/Varunittigi/Neural-Style-transfer

A Neural Algorithm of Artistic Style

https://github.com/VasanthManiVasi/MusicTransformer.jl

This Time with Feeling: Learning Expressive Musical Performance

https://github.com/VedantDere0104/Cycle_GAN

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

https://github.com/VegB/VLN-Transformer

Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation

https://github.com/Vernacular-ai/Multimodal-Slu

Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning

https://github.com/Vigneshthanga/stackGAN-v2

Generative Adversarial Text to Image Synthesis

https://github.com/Vill-Lab/2022-TIP-HCGA

Deep High-Resolution Representation Learning for Human Pose Estimation

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