https://github.com/mindspore-ecosystem/mindcv/blob/main/mindcv/models/vit.py
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/mingsun-tse/good-da-in-kd
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/mingxingtan/efficientnet
https://github.com/miramind/efficientnets_pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mit-han-lab/litepose
https://github.com/mjhucla/TF-mRNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mjhucla/mRNN-CR
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mjoannou/audiovisual-moments-in-time
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mlmed/chester-xray
On the limits of cross-domain generalization in automated X-ray prediction
https://github.com/mlmed/dl-web-xray
On the limits of cross-domain generalization in automated X-ray prediction
https://github.com/mlvccn/bmtc_transferattackvid
https://github.com/mlvlab/dapt
https://github.com/mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection
https://github.com/mminamina/Transfer_Learning_ResNet18---Keypoints_Detection
https://github.com/mnguyen0226/image-augmentation-dnn-performance
https://github.com/mnikitin/EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mnikitin/Shift-Invariant-CNNs
https://github.com/mobilesec/arcface-tensorflowlite
https://github.com/moddy2024/resnet-9
https://github.com/modeeric/eegvit-tcnet
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/modelhub-ai/vgg-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/modestyachts/ImageNetV2
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/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/mr-eggplant/eata
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/mr-eggplant/sar
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/mrzzy/np-dl-assign-1
https://github.com/mszpc/resnet18
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.