https://github.com/MindCode-4/code-14/tree/main/AVA_hpa
RandAugment: Practical automated data augmentation with a reduced search space
Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 570
The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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-8/tree/main/openfashionclip-vision-and-language
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
https://github.com/MindSpore-paper-code-2/code2/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet34
https://github.com/MindSpore-paper-code-2/code400/tree/main/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code1/tree/main/autoaugment
https://github.com/MindSpore-paper-code-3/code2/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code5/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnext152_64x4d
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/MindSpore-paper-code-3/code6/tree/main/autoaugment
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/DepthNet
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/csd
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition
https://github.com/MindSpore-paper-code-3/code8/tree/main/darknet53
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-paper-code-3/code8/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/MindSpore-scientific-2/code-1/tree/main/MVD
MVDG: A Unified Multi-view Framework for Domain Generalization
https://github.com/MindSpore-scientific-2/code-10/tree/main/rand_conv-master
Robust and Generalizable Visual Representation Learning via Random Convolutions
https://github.com/MindSpore-scientific-2/code-11/tree/main/Translation-Invariant
https://github.com/MindSpore-scientific-2/code-11/tree/main/rand_conv-master
Robust and Generalizable Visual Representation Learning via Random Convolutions
https://github.com/MindSpore-scientific-2/code-4/tree/main/rand_conv-master
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/MindSpore-scientific/code-11/tree/main/rand_conv-master
Robust and Generalizable Visual Representation Learning via Random Convolutions
https://github.com/MindSpore-scientific/code-6/tree/main/openfashionclip-vision-and-language
OpenFashionCLIP: Vision-and-Language Contrastive Learning with Open-Source Fashion Data
https://github.com/MindSpore-scientific/code-7/tree/main/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/MindSpore-scientific/code-9/tree/main/MVD
MVDG: A Unified Multi-view Framework for Domain Generalization
https://github.com/MioChiu/ResNet_TensorFlow
https://github.com/MisaOgura/flashtorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MishaLaskin/vq-vae
https://github.com/MishaLaskin/vqvae
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MustaphaBounoua/minde
Beyond Normal: On the Evaluation of Mutual Information Estimators
https://github.com/NASA-NeMO-Net/NeMO-Net
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.