https://github.com/modestyachts/ImageNetV2
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Domain Adaptation
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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/mohamedameen93/German-Traffic-Sign-Classification-Using-TensorFlow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mohammedsb/dinov2formedical
https://github.com/mojesty/professor_forcing
Professor Forcing: A New Algorithm for Training Recurrent Networks
https://github.com/monkey0head/Domain_Adaptation_thesis
https://github.com/morganmcg1/stanford-cars
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/morrisxu-driving/video-music_cross-modal_retrival
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mousecpn/Boosting-R-CNN-Reweighting-R-CNN-Samples-by-RPN-s-Error-for-Underwater-Object-Detection
Robust Object Detection of Underwater Robot based on Domain Generalization
https://github.com/mousecpn/Domain-Generalization-via-Image-Stylization
Frustratingly Simple Domain Generalization via Image Stylization
https://github.com/mousecpn/L2A-OT
Learning to Generate Novel Domains for Domain Generalization
https://github.com/mousecpn/boosting-r-cnn
Robust Object Detection of Underwater Robot based on Domain Generalization
https://github.com/mr-bulb/DL_basic_github
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mrzzy/np-dl-assign-1
https://github.com/ms903-github/ACAL-pytorch
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
https://github.com/mszpc/osnet
Learning Generalisable Omni-Scale Representations for Person Re-Identification
https://github.com/mszpc/resnet18
https://github.com/mtancak/PyTorch-ViT-Visual-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/mtancak1/PyTorch-ViT-Visual-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/mujiyantosvc/Facial-Expression-Recognition-FER-for-Mental-Health-Detection-
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/murufeng/awesome_lightweight_networks
https://github.com/musifahamran/FYP
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/muskang48/Speaker-Diarization
https://github.com/mustafa1728/TA3N-Lightning
https://github.com/mustafa1728/TA3N-Lightning-Kitchen_Domain_Adaptation
https://github.com/muzairkhattak/multimodal-prompt-learning
https://github.com/muzairkhattak/protext
https://github.com/mvenouziou/Project-Attention-Is-What-You-Get
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mvrcii/capsule_vision_challenge_2024
https://github.com/mx-mark/videotransformer-pytorch
https://github.com/mzeromiko/vmamba
https://github.com/n-akram/SafeML
SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure
https://github.com/nachiket273/VisTrans
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/nachiket273/Vision_transformer_pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/nachiket273/pytorch_resnet_rs
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/nagadomi/kaggle-cifar10-torch7
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/najlaeLemrabet/FacialKeypointsDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.