https://github.com/ZacharyGoshen/cifar-10-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/Zachdr1/CNNs
https://github.com/ZackPashkin/YOLOv3-EfficientNet-EffYolo
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Ze-Yang/Context-Transformer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zhachory1/MusicNST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZhouDaShan123/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/a1600012888/YOPO-You-Only-Propagate-Once
https://github.com/aHapBean/PCP-MAE
https://github.com/abaietto/neural_ode_classification
https://github.com/abcp4/DAPytorch
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/abhuse/pytorch-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/abuchin/resnet_sign
https://github.com/aby2s/sharpmask
https://github.com/acmiyaguchi/birdclef-2022
https://github.com/adam-mehdi/MuarAugment
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/adapt-python/adapt
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
https://github.com/adldotori/ResNet
https://github.com/affjljoo3581/deit3-jax
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/agrija9/deep-unsupervised-domain-adaptation
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
https://github.com/ahmad0790/svhn-multi-digit-address-recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ahmed-alllam/Equinox/blob/main/examples/vision_transformer.ipynb
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/ahmedbhna/VGG_Paper
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/airplane2230/keras_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/ajaystar8/PDRUNet-PyTorch
https://github.com/ajfisch/few-shot-cp
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
https://github.com/ajithvallabai/getsetgo_keras-beginner
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akamaster/pytorch_resnet_cifar10
https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akhadangi/EM-net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/akjgskwi/mixup_chainer
https://github.com/akommini/Spoken-Numeric-Digit-detection
https://github.com/alalagong/LEDNet
https://github.com/alanqrwang/nwhead
Learning Invariant Representations with a Nonparametric Nadaraya-Watson Head
https://github.com/albertomontesg/keras-model-zoo
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/albumentations-team/albumentations
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/alecwangcq/KFAC-Pytorch
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.