Papers with Code 아카이브

2D Classification

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코드

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

https://github.com/JensSettelmeier/EfficientDet-DeepSORT-Tracker

EfficientDet: Scalable and Efficient Object Detection

https://github.com/JeonMinkyu/MAML_Pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Jeongseungwoo/Singing-Style-transfer

Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks

https://github.com/Jeongseungwoo/U_net

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JeongsolKim/BiS400_term_project

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

https://github.com/Jeongyun-Lee-0423/Classification

Deep Residual Learning for Image Recognition

https://github.com/JepsonWong/Text_Classification

Overview of CAIL2018: Legal Judgment Prediction Competition

https://github.com/Jeremy-lf/RT-DATR

RT-DATR:Real-time Unsupervised Domain Adaptive Detection Transformer with Adversarial Feature Learning

https://github.com/JeremyCCHsu/vae-npvc

Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder

https://github.com/JeremyCCHsu/vae4vc

Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder

https://github.com/JesonZhang822/Dogs-VS-Cats-Project

Deep Residual Learning for Image Recognition

https://github.com/JetRunner/PABEE

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/JeyesHan/P2LR

Deep Residual Learning for Image Recognition

https://github.com/Jia-Research-Lab/GridMask

GridMask Data Augmentation

https://github.com/JiaPeng1234/MRI-Segmentation-Transformer

Non-local Neural Networks

https://github.com/Jiachenyin1/Yolov3-U-attention-permuting

YOLOv3: An Incremental Improvement

https://github.com/JialeCao001/D2Det

https://github.com/JialeCao001/HSD

https://github.com/JiamingLv/WKD

Wasserstein Distance Rivals Kullback-Leibler Divergence for Knowledge Distillation

https://github.com/JiangWenPL/multiperson

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/JiaquanYe/TableMASTER-mmocr

Image-based table recognition: data, model, and evaluation

https://github.com/JiaweiZhao-git/DASNet

Is Depth Really Necessary for Salient Object Detection?

https://github.com/Jiaxin-Ye/DepMamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/JifeiWang-WHU/Pytorch_Building_extraction

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/JimmyDoan1309/Neural-Style-Transfer

A Neural Algorithm of Artistic Style

https://github.com/JimmyLauren/caffe_ssd_mofified

SSD: Single Shot MultiBox Detector

https://github.com/JinLi711/Attention-Augmented-Convolution

CBAM: Convolutional Block Attention Module

https://github.com/JinLi711/Convolution_Variants

CBAM: Convolutional Block Attention Module

https://github.com/JinfengZhang1994/DynaSLAM

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

https://github.com/JingtongSu/sanity-checking-pruning

SNIP: Single-shot Network Pruning based on Connection Sensitivity

https://github.com/Jintao-Huang/Darknet53_PyTorch

YOLOv3: An Incremental Improvement

https://github.com/Jintao-Huang/EfficientDet_PyTorch

EfficientDet: Scalable and Efficient Object Detection

https://github.com/Jintao-Huang/EfficientNet_PyTorch

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/JinyongJeong/DeeplabV3_Apolloscape_and_CityScape

Rethinking Atrous Convolution for Semantic Image Segmentation

https://github.com/JinyuanLiu-CV/TarDAL

Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection

https://github.com/JisuHann/MEME-Tracking

YOLOv4: Optimal Speed and Accuracy of Object Detection

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