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/alexsax/midlevel-reps
https://github.com/alexwdong/IncubatorCVProject
https://github.com/alibaba/EasyCV
https://github.com/alibaba/cluster-contrast
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/alibaba/cluster-contrast-reid
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/alibaba/easyrobust/tree/main/examples/imageclassification/imagenet/drvit
Discrete Representations Strengthen Vision Transformer Robustness
https://github.com/alicebizeul/pmae
https://github.com/alililia/ascend_FaceAttribute
https://github.com/alililia/ascend_SE-Net
https://github.com/alililia/ascend_autoaugment
https://github.com/alililia/ms_contrib/tree/main/ascend_vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alililia/ms_contrib/tree/main/gpu_vgg19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/alililia/ms_extend/tree/main/gpu_efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/alililia/ms_extend/tree/main/gpu_resnet
https://github.com/alililia/vit_base_Ascend
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/alililia/vit_base_GPU
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/alisha17/benchmarks
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/allenai/robustnav
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/alrojo/lasagne_residual_network
https://github.com/alvarobartt/serving-pytorch-models
https://github.com/amamov/vgg16-vs-resnet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aman-garg0001/fruit-classifier
https://github.com/amazon-research/long-short-term-transformer
https://github.com/amazon-science/long-short-term-transformer
https://github.com/ambityga/memsem
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amdegroot/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amerch/CIFAR100-Training
https://github.com/amirdy/dog-breed-classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/amogh7joshi/engagement-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/fer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/plant-health-detection
https://github.com/andersoncarlosfs/image_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/andreasveit/densenet-pytorch
https://github.com/andrijdavid/clinical-brain-interface-2020
https://github.com/andychinka/dcase-challenge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.