https://github.com/innat/LearnedResizer-Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Papers with Code 아카이브
Domain Generalization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 21
- 보존된 평가 기록
- 570
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/innat/VideoMAE
https://github.com/interestingzhuo/foodretreival
https://github.com/intsco/am-segmentation
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/isaaccorley/hydro-foundation-model
https://github.com/isaachaw/GrabCarRecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/isaacschaal/SG_training
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning
https://github.com/islamosmanubc/MedMAE
https://github.com/itijyou/ademxapp
https://github.com/iver56/audiomentations
https://github.com/j-nowak/covid-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/j3soon/arxiv-utils
https://github.com/jacobgil/vit-explain
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/jaketae/mlp-mixer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/james77777778/keras-image-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jameswang287/Car-Detection
https://github.com/janZub-AI/EmotionDetectionVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jankrepl/mildlyoverfitted
https://github.com/jason90330/EdgeFinal
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/jayroxis/ordering-in-graph-generation
https://github.com/jday96314/Kuzushiji
https://github.com/jeonsworld/ViT-pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/jessica3724/Keras_VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jessiemjones/TF-2.0-Neural-Style-Transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jiajunhua/facebookresearch-Detectron
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jiangtaoxie/So-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/jianshen92/stanford-car-grab-challenge
https://github.com/jiequancui/Parametric-Contrastive-Learning
https://github.com/jihaoxuanye/MetaPRD
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
https://github.com/jis478/Tensorflow/tree/master/TF2.0/Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jis478/cutmix_tensorflow2
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/jiweibo/imagenet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jm199504/VGG-NET
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jmaronas/calibration_MixupDNN_ARCLoss.pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.