https://worksheets.codalab.org/worksheets/0xe3eb416773ed4883bb737662b31b4948
공식 코드Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
Papers with Code 아카이브
2D Semantic Segmentation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://bitbucket.org/tsungyu/bcnn
https://github.com/04RR/SOTA-Vision
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
https://github.com/0bserver07/Keras-SegNet-Basic
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
https://github.com/0bserver07/One-Hundred-Layers-Tiramisu
The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
https://github.com/0jason000/MobileNet_V2
https://github.com/0jason000/MobilleNetV3
https://github.com/0xzayd/Wnet-cGAN
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/1044197988/TF.Keras-Commonly-used-models
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/163GitHub/AI
https://github.com/16xccheng/keras-unet
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/2020-sk49/VFOST
https://github.com/2023-MindSpore-1/ms-code-118
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-122
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-15/tree/main/fcn-4
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/Auto-DeepLab
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/adelaide_ea
Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/2023-MindSpore-1/ms-code-16/tree/main/augvit
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
https://github.com/2023-MindSpore-1/ms-code-167
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-170
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-183
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-207
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/cnn_direction_model
https://github.com/2023-MindSpore-1/ms-code-210/tree/main/cnnctc
https://github.com/2023-MindSpore-1/ms-code-211/tree/main/dlinknet
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FCN8s
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/fastscnn
https://github.com/2023-MindSpore-1/ms-code-214/tree/main/ibnnet
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/OSVOS
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/PSPNet
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/res2net_deeplabv3
Rethinking Atrous Convolution for Semantic Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/UNet3%2B
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_mobilenetV2
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_mobilenetV2_FPNlite
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/tinydarknet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.