https://github.com/A-Telfer/AugKey
RandAugment: Practical automated data augmentation with a reduced search space
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/AIOSYM/dl-model-gallery
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AKASH2907/Forgery-Classification-via-Domain-Adaptation
https://github.com/AbdallahMGomaa/PatchNet
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition
https://github.com/Abdulrahman-Adel/Real-Life-Violence-Detection
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Acassarino/Covid_xray_dataset
On the limits of cross-domain generalization in automated X-ray prediction
https://github.com/Acassarino/Test_Covid
On the limits of cross-domain generalization in automated X-ray prediction
https://github.com/Aedelon/ViT-PyTorch-Replication
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Afsaan/CNN-Architectures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AlanChou/Adversarial-Training-for-Free
https://github.com/AlassaneSakande/A-ConvNet-of-2020s
https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/graphix
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
https://github.com/AlifAshrafee/ViT-pytorch-for-Cooking-State-Recognition
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/AmirHussein96/Simplified-DSN
https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Anderies/stochastic-constant-pruninglayers
https://github.com/AngusG/bn-advex-zhang-fixup
https://github.com/Aniket7/VGGNet-CNN-architecture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Anuranjan-Kumar/kaggle_cats-vs-dogs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ArifRaihan/Deeper-CNN-Architectures-for-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ArsenalCheng/Meta-Adapter
https://github.com/Asthestarsfalll/ConvNeXt-MegEngine
https://github.com/Asthestarsfalll/IBNNet-MegEngine
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/Asthestarsfalll/MAE-MegEngine
https://github.com/AveryGriffin/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/AyushAniket/ResNET-and-various-normalizations
https://github.com/BIG-CHENG/FaceRec
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/VOLO
https://github.com/BR-IDL/PaddleViT/blob/main/image_classification/ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/ConvNeXt
https://github.com/BUPT-PRIV/MAE-priv
https://github.com/Baichenjia/Resnet
https://github.com/BaiqiangGit/15minCode
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Bao-Jiarong/ResNet
https://github.com/BeanGreen247/Python-AI-Arts
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BebDong/MXNetSeg
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.