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2D Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CarloLepelaars/filter_response_normalization_keras
https://github.com/CarpdiemLiang/style_transfer
https://github.com/CarryHJR/Nested-UNet
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/CarstenIsert/DeepBurn
https://github.com/Cassie94/LSTD
https://github.com/CatOneTwo/WSDDN-PyTorch
https://github.com/CeLaMbDa/DSOD_keras
DSOD: Learning Deeply Supervised Object Detectors from Scratch
https://github.com/Cedric-Perauer/Deformable_Detr_PIL
Deformable DETR: Deformable Transformers for End-to-End Object Detection
https://github.com/CellEight/Pytorch-Adaptive-Instance-Normalization
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/CellProfiling/HPA-Special-Prize
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
https://github.com/CellSMB/EM-net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Chad4545/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Chamchis/AICCTV
https://github.com/ChandhiniG/Generate-Stylized-Image-from-Edges
https://github.com/Chang-Chia-Chi/TrackNet-Badminton-Tracking-tensorflow2
https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models
https://github.com/Charmve/Semantic-Segmentation-PyTorch/blob/master/models/u_net.py
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Chasing-After-AI/AI-papers
https://github.com/Chefibadly/Language-translation-model-training-and-implementation-using-flask
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
https://github.com/ChelseaGH/sidewalk_prototype_AI_Hub
https://github.com/Chen-Wang-CUHK/DialSum-with-SUFM-and-FR
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
https://github.com/ChenWu98/Point-Then-Operate
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
https://github.com/ChengBinJin/Real-time-style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ChengBinJin/U-Net-TensorFlow
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ChengLeiWei/garbage-detect-using-YOLOv3-pytorch
https://github.com/ChengWeiGu/ResNeSt-Pytorch
https://github.com/Chenglin-Yang/LESA_classification
Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms
https://github.com/Chenglin-Yang/LESA_detection
Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms
https://github.com/Chenzixi1/D4LCNpaddle
Learning Depth-Guided Convolutions for Monocular 3D Object Detection
https://github.com/ChestnutLi/tf-faster-rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/ChetanTayal138/HAR-Web
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
https://github.com/ChetanTayal138/NeuralStyleTransfer
https://github.com/ChielBruin/ros_faster_rcnn
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/China-UK-ZSL/ZS-F-VQA
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
https://github.com/ChintanThacker/Facial_Expression_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.