Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/khurram702/StyleBasedGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kingcong/alexnet
https://github.com/kingcong/convnext
https://github.com/kingcong/convnext-
https://github.com/kingcong/gpu_FaceAttribute
https://github.com/kingcong/gpu_SE-Net
https://github.com/kingcong/resnet
https://github.com/kingcong/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/kiniavinash/ACAL
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
https://github.com/kiranu1024/deep-learning
https://github.com/kishanAk21/Skin-Cancer-Image-Classification
https://github.com/kishorkuttan/traffic_net
https://github.com/kit-mrt/masked-fusion-360
https://github.com/km1414/CNN-models
https://github.com/kmzzhang/nbi
https://github.com/kobiso/CBAM-tensorflow
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/kobiso/CBAM-tensorflow-slim
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/kondratevakate/fmri-fader-net
Fader Networks for domain adaptation on fMRI: ABIDE-II study
https://github.com/konny0311/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/konstantinos-p/image_classification_SOTA
https://github.com/kornia/kornia
https://github.com/kornia/kornia/blob/e4a54c2d4fd9b8eaa88dec50061e33f7aee5d959/kornia/filters/blur_pool.py
https://github.com/koshian2/ResNet-MultipleFramework
https://github.com/kreimanlab/HumanIntentionInferenceZeroShot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/krishnakarthi/COVID-19_Prediction
https://github.com/krumo/Detectron-DA-Faster-RCNN
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/krumo/Domain-Adaptive-Faster-RCNN-PyTorch
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/ksaito-ut/OPDA_BP
https://github.com/ksaito-ut/atda
https://github.com/kschwethelm/hyperboliccv
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/kumasento/gconv-prune
Efficient Structured Pruning and Architecture Searching for Group Convolution
https://github.com/kun-woo-park/Deeplearning_project_STL_10
https://github.com/kwantommy/SE-ResNeXt-image-classification
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/kwantommy/fgvc6-kaggle-cassava-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kwotsin/TensorFlow-ENet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.