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2D Classification

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

https://github.com/abman23/pmnet

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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

Adversarially Regularized Autoencoders

https://github.com/abstatic/ire_project18

Automatic Argumentative-Zoning Using Word2vec

https://github.com/abuchin/resnet_sign

Deep Residual Learning for Image Recognition

https://github.com/aby2s/sharpmask

Learning to Segment Object Candidates

https://github.com/ac-optimus/ESDC_IntelCup2018

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/ac-rodriguez/privilegedpooling

Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up

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

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

https://github.com/achalddave/segment-any-moving

Towards Segmenting Anything That Moves

https://github.com/achen353/Taiwanese-Traffic-Object-Detection

YOLOv3: An Incremental Improvement

https://github.com/achen353/face-mask-detector

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/achiyaj/vqa-sandbox

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/acht7111020/DSMAP

Domain-Specific Mappings for Generative Adversarial Style Transfer

https://github.com/adairliulei/yolov3

YOLOv3: An Incremental Improvement

https://github.com/adam-mehdi/MuarAugment

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/adam123wu/edge-yolo

YOLOv3: An Incremental Improvement

https://github.com/adamdad/dery

Deep Incubation: Training Large Models by Divide-and-Conquering

https://github.com/adamlakatos0407/ScaledYOLOv4

Scaled-YOLOv4: Scaling Cross Stage Partial Network

https://github.com/adamvest/keypoint-baseline

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/adamwhitakerwilson/speaker_separation

Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation

https://github.com/addisonklinke/pytorch-architectures/blob/377548954f13d32e45e5641396e3839a9103395b/torcharch/conv.py

Feature Pyramid Networks for Object Detection

https://github.com/addisonklinke/pytorch-architectures/blob/master/torcharch/conv.py

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://github.com/adeelz92/DaD-Framework

Detect-and-describe: Joint learning framework for detection and description of objects

https://github.com/adept-thu/SATGCN

S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection

https://github.com/aditya30394/Person-Re-Identification

Random Erasing Data Augmentation

https://github.com/adityaarun1/pytorch_fast-er_rcnn

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/adityajn105/brain-tumor-segmentation-unet

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/adityarajsahu/UNet-Implementation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/adldotori/ResNet

Deep Residual Learning for Image Recognition

https://github.com/adlnlp/doc_gcn

PubLayNet: largest dataset ever for document layout analysis

https://github.com/adnanalam53/cycleGAN

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

https://github.com/adrienangeli/dino

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/adursun/wsddn.pytorch

Weakly Supervised Deep Detection Networks

https://github.com/afruehstueck/tileGAN

TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

https://github.com/afzalahmad0203/Numpy-Shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/afzalahmad0203/Tensorflow-Shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

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