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/SifatMd/Research-Papers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SikaStar/IDM
https://github.com/SnailDev/github-hot-hub
https://github.com/SparkZu/RadioLLM
https://github.com/Spoorthy-gunda/Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SrinjaySarkar/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/SunDoge/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SupratimH/neural-style-transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SupreethRao99/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/SyogoShibuya/Chainer-Cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/TACJu/TransFG
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/TRI-ML/vidar
Depth Field Networks for Generalizable Multi-view Scene Representation
https://github.com/Taoudi/DataAugmentation
https://github.com/Taoudi/ImbalancedData
https://github.com/Taoudi/LotteryTicketHypothesis
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ThanasisMattas/smartflow
https://github.com/Thanusan19/Vision_Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/The-AI-Summer/self_attention
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/TheTensorDude/vision_transformer_tf
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Thehunk1206/Covid-19-chest-X-ray
https://github.com/Thehunk1206/Covid-19-covidcnn
https://github.com/ThomasWangY/2024-AAAI-HPT
https://github.com/TianshuXie/Cut-Thumbnail
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/TillBeemelmanns/auto-augment-tf2-operations
https://github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learning
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/Tools4Project/4501Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/TotalVariation/Flattenet
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/TowardHumanizedInteraction/TripletFace
https://github.com/TravisLeeTS/grabcvchallenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Trustworthy-AI-Group/TransferAttack
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
https://github.com/Trustworthy-ML-Lab/Interpretability-Guided-Defense
Efficient and Effective Augmentation Strategy for Adversarial Training
https://github.com/TuSimple/resnet.mxnet
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/UKPLab/coling18-multimodalSurvey
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/UdbhavPrasad072300/Transformer-Implementation
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.