Papers with Code 아카이브
Synthetic-to-Real Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.
<span style="color:grey; opacity: 0.6">( Image credit: CYCADA )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet101
https://github.com/2024-MindSpore-1/Code7/tree/main/centernet_resnet50_v1
https://github.com/3P2S/arcface
https://github.com/AKASH2907/Forgery-Classification-via-Domain-Adaptation
https://github.com/Abhaya1998/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/Anderies/stochastic-constant-pruninglayers
https://github.com/AngusG/bn-advex-zhang-fixup
https://github.com/AyushAniket/ResNET-and-various-normalizations
https://github.com/Baichenjia/Resnet
https://github.com/Bao-Jiarong/ResNet
https://github.com/Bingwen-Hu/DRIT
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/Burf/ResNet-Tensorflow2
https://github.com/CVxTz/face_age_gender
https://github.com/CanshangD/ResNet_tensorflow2.0
https://github.com/CellSMB/EM-net
https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models
https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest
https://github.com/CodeChefVIT/VOID
https://github.com/CorneliusHsiao/FoodMethodGAN
https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork
https://github.com/CryptoSalamander/pytorch_paper_implementation
https://github.com/D-X-Y/ResNeXt-DenseNet
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/DaikiTanak/manifold_mixup
https://github.com/Darth-Kronos/Unsupervised-Domain-Adaptation
https://github.com/Deci-AI/super-gradients
https://github.com/DeepMark/deepmark
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/Duplums/bhb10k-dl-benchmark
https://github.com/E-DEEP/PapersReview
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
https://github.com/Elman295/Maximum-Classifier-Discrepancy-for-Unsupervised-Domain-Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/EricAlcaide/MiniFold
https://github.com/EstherBear/implementation-of-pruning-filters
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.