https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Unpaired_Image_to_Image_Translation_using_Cycle_Consistent_Adversarial_Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Papers with Code 아카이브
Translation
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/Mayurji/Image-Classification-PyTorch
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/Maz101/Bert
https://github.com/Maz101/pretrainingdataset
https://github.com/Medabid1/TravelGAN
TraVeLGAN: Image-to-image Translation by Transformation Vector Learning
https://github.com/Mees-Molenaar/protein_location
https://github.com/MehmetBarutcu/PHM-Paper-Implementation
https://github.com/MichaelZhouwang/LMlexsub
https://github.com/MichalKacprzak99/reconstruction_particle_mass_spectra
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
https://github.com/Microsoft/NPMT
https://github.com/Mind23-2/MindCode-132
https://github.com/Mind23-2/MindCode-151
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA
https://github.com/Mind23-2/MindCode-160
https://github.com/Mind23-2/MindCode-164
https://github.com/Mind23-2/MindCode-5/tree/main/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Mind23-2/MindCode-61
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Mind23-2/MindCode-84
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/Mind23-2/MindCode-85
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
https://github.com/MindCode-4/code-11/tree/main/Fixup-Initialization
Fixup Initialization: Residual Learning Without Normalization
https://github.com/MindCode-4/code-13/tree/main/res_mlp_ms
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/MindCode-4/code-2/tree/main/persimmon
Transformers without Tears: Improving the Normalization of Self-Attention
https://github.com/MindCode-4/code-3/tree/main/fsmt
Multi-Task Neural Models for Translating Between Styles Within and Across Languages
https://github.com/MindCode-4/code-4/tree/main/plbart
NatGen: Generative pre-training by "Naturalizing" source code
https://github.com/MindCode-4/code-4/tree/main/reformer
RegNet: Multimodal Sensor Registration Using Deep Neural Networks
https://github.com/MindCode-4/code-5/tree/main/transformer
https://github.com/MindCode-4/code-7/tree/main/Fixup-Initialization
Fixup Initialization: Residual Learning Without Normalization
https://github.com/MindCode-4/code-8/tree/main/NeBLa
NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs
https://github.com/MindCode-4/code-8/tree/main/optimize-coordconv
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
https://github.com/MindCode-4/code-8/tree/main/res_mlp_ms
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/MindSpore-paper-code-2/code2/tree/main/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/MindSpore-paper-code-2/code3/tree/main/STGAN
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
https://github.com/MindSpore-paper-code-2/code3/tree/main/U-GAT-IT
https://github.com/MindSpore-paper-code-2/code400/tree/main/CycleGAN
https://github.com/MindSpore-paper-code-3/code5/tree/main/rfcn
R-FCN: Object Detection via Region-based Fully Convolutional Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/dgcnet_res101
https://github.com/MindSpore-paper-code-3/code8/tree/main/dnet_nas
D-Net: Siamese based Network with Mutual Attention for Volume Alignment
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.