https://github.com/xden2331/attentive_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/xhan77/AdaptaBERT
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
https://github.com/xhlulu/arxiv-assistant
https://github.com/xiaofeng-han-res/mf-rv
Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision
https://github.com/xiaowillow/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/xiaowillow/AdaptSegNet1
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/xiaoyufenfei/LEDNet
https://github.com/xinge008/SCDA
https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters
https://github.com/xiuyu0000/new_papers_codes/tree/main/vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/xiuyu0000/papers_with_examples/tree/main/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo
https://github.com/xiuyu0000/tutorials/tree/main/resnet
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/backbones/efficientnet.py
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py
https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/vgg.py
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/xjtu-xgu/adversarial-reweighting-for-partial-domain-adaptation
https://github.com/xjtu-xgu/otcs
https://github.com/xkazm/asfa
Casting a BAIT for Offline and Online Source-free Domain Adaptation
https://github.com/xmed-lab/cardiacnet
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xmed-lab/m3-uda
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/attention/OutlookAttention.py
https://github.com/xplip/pixel
https://github.com/xslidi/EfficientNets_ddl_apex
https://github.com/xuanyuzhou98/SqueezeSegV2
https://github.com/xufanxiong/classification-of-caltech-256
https://github.com/xxlya/Fed_ABIDE
https://github.com/xycforgithub/MultiTask-MRC
Multi-Task Deep Neural Networks for Natural Language Understanding
https://github.com/yahoo/open_nsfw
https://github.com/yakhyo/EfficientNet-PyTorch
https://github.com/yakhyo/gaze-estimation
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
https://github.com/yangarbiter/dp-dg
https://github.com/yangboz/TransferLearning4Dentist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yangheng95/LC-ABSA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.