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Synthetic-to-Real Translation

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하위 태스크: 0
평가표: 4
보존된 평가 기록: 119

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/swasun/VQ-VAE-images

Deep Residual Learning for Image Recognition

https://github.com/swetak20/Deep_into_CNNs

Deep Residual Learning for Image Recognition

https://github.com/syiin/ann_cancer_detection

Deep Residual Learning for Image Recognition

https://github.com/syiin/resnet_cancer_detection

Deep Residual Learning for Image Recognition

https://github.com/t0930198/backup

Deep Residual Learning for Image Recognition

https://github.com/tachitachi/GradientReversal

Domain-Adversarial Training of Neural Networks

https://github.com/taki0112/DRIT-Tensorflow

Diverse Image-to-Image Translation via Disentangled Representations

https://github.com/tanjeffreyz/deep-residual-learning

Deep Residual Learning for Image Recognition

https://github.com/tanpinquan/EE5934_2

Learning to Adapt Structured Output Space for Semantic Segmentation

https://github.com/tcheung99/ResNet_MiniProject

Deep Residual Learning for Image Recognition

https://github.com/tensorflow/models

Deep Residual Learning for Image Recognition

https://github.com/tensorflow/models/tree/master/research/deeplab

Deep Residual Learning for Image Recognition

https://github.com/tensorflow/models/tree/master/research/slim

Deep Residual Learning for Image Recognition

https://github.com/tensorpack/tensorpack

Deep Residual Learning for Image Recognition

https://github.com/tensorpack/tensorpack/tree/master/examples/ResNet

Deep Residual Learning for Image Recognition

https://github.com/the-super-toys/glimpse-models

Deep Residual Learning for Image Recognition

https://github.com/thughost2/Padam

Deep Residual Learning for Image Recognition

https://github.com/thuml/Transfer-Learning-Library

Domain-Adversarial Training of Neural Networks

https://github.com/tiagoCuervo/JapaNet

Deep Residual Learning for Image Recognition

https://github.com/timgaripov/asa

Domain-Adversarial Training of Neural Networks

https://github.com/tkhkaeio/CyCADA

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

https://github.com/towhee-io/towhee

Deep Residual Learning for Image Recognition

https://github.com/trachpro/arcface-tf2

Deep Residual Learning for Image Recognition

https://github.com/uclaml/Padam

Deep Residual Learning for Image Recognition

https://github.com/ultralytics/yolov5

Deep Residual Learning for Image Recognition

https://github.com/usnistgov/image-classification-resnet50

Deep Residual Learning for Image Recognition

https://github.com/usnistgov/image-regression-resnet50

Deep Residual Learning for Image Recognition

https://github.com/uw-hai/limeade

Deep Residual Learning for Image Recognition

https://github.com/varshaneya/Res-SE-Net

Deep Residual Learning for Image Recognition

https://github.com/vayuvegula/kerasModels

Deep Residual Learning for Image Recognition

https://github.com/vcoyette/DANN

Domain-Adversarial Training of Neural Networks

https://github.com/venkateshdudipalli/Self-Driving-Car

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/vgangal101/resnet_models

Deep Residual Learning for Image Recognition

https://github.com/vict0rsch/ArxivTools

Domain-Adversarial Training of Neural Networks

https://github.com/vict0rsch/PaperMemory

Domain-Adversarial Training of Neural Networks

https://github.com/vict0rsch/arxiv-pdf-abs

Domain-Adversarial Training of Neural Networks

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