https://github.com/asyml/vision-transformer-pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Papers with Code 아카이브
Domain Adaptation
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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/attm/tensorflow_advent
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/automl/nes
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/autonomobil/SCDND-P2_Traffic-Sign-Classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/avinash31d/paper-implementations/tree/main/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/avisekiit/wacv_2019
https://github.com/avocardio/resnet_vs_convnext
https://github.com/axmoyal/Steganalysis-with-CNN
https://github.com/ayushdabra/drone-images-semantic-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/lunar-landscape-images-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/retinal-oct-images-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/gan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/azshue/TPT
https://github.com/b8goal/PIRL-Residual-network
https://github.com/bailvwangzi/repulsion_loss_ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bamps53/convnext-tf
https://github.com/baolp/demoireing_with_focused_and_defocused_images_pairs
Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
https://github.com/barisbatuhan/dass_det_inference
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
https://github.com/barisbatuhan/dass_detector
Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
https://github.com/barisozmen/deepaugment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/barmayo/spatial_attention
https://github.com/bartoszpanek1/Real-time-emotion-recognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bbdamodaran/deepJDOT
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
https://github.com/bbzhu-jy16/DomainStudio
https://github.com/bclwan/MRI_Brain_Segmentation
https://github.com/belaalb/TI-DG
https://github.com/belaalb/g2dm
https://github.com/ben-davidson-6/fixup
https://github.com/beneroth13/dinov2
https://github.com/bentrevett/pytorch-image-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bepierre/SpeechVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bertrandlalo/deep_dog_breeds
https://github.com/bespontaneous/proteus-pytorch
https://github.com/bethgelab/InDomainGeneralizationBenchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.