https://github.com/MatthewAbugeja/osnet
Learning Generalisable Omni-Scale Representations for Person Re-Identification
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Maveric4/pr19aaw01
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Flow_Straight_and_Fast_Learning_to_Generate_and_Transfer_Data_with_Rectified_Flow
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/MaxwellHeller/coffee_fraud_detection
https://github.com/Mayurji/Image-Classification-PyTorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Media-Smart/vedaseg
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet
https://github.com/Meghraj-Webllisto/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MiX-S/resnet_cifar10
https://github.com/MichaelWangfc/distributed-tensorflow-resnet
https://github.com/MichiganLu/SuperPoint_tensorflow_testing
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/Microsoft/EconML
https://github.com/Mid-Push/Moving-Semantic-Transfer-Network
https://github.com/Mind23-2/MindCode-1
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-119
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-138
https://github.com/Mind23-2/MindCode-3/tree/main/ibnnet
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-31
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-37
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-4/tree/main/resnet_ipl
https://github.com/Mind23-2/MindCode-4/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-52
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/Mind23-2/MindCode-67
https://github.com/Mind23-2/MindCode-70
https://github.com/Mind23-2/MindCode-89
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/MindCode-4/code-10/tree/main/Alexnet-ABeffect
https://github.com/MindCode-4/code-12/tree/main/learning-an-explicit-hyperparameter
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
https://github.com/MindCode-4/code-12/tree/main/multi-adversarial-domain-adaptation
https://github.com/MindCode-4/code-14/tree/main/AVA_hpa
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/MindCode-4/code-3/tree/main/convnext
https://github.com/MindCode-4/code-4/tree/main/rembert
https://github.com/MindCode-4/code-5/tree/main/superpoint
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/MindCode-4/code-7/tree/main/multi-adversarial-domain-adaptation
https://github.com/MindCode-4/code-8/tree/main/openfashionclip-vision-and-language
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.