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/kwantommy/SE-ResNeXt-image-classification
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/kwantommy/fgvc6-kaggle-cassava-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kwotsin/TensorFlow-ENet
https://github.com/kyle-dorman/bayesian-neural-network-blogpost
https://github.com/kynkaat/improved-precision-and-recall-metric
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lRomul/argus-freesound
https://github.com/lab-midas/med_segmentation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lastmile-ai/aiconfig
https://github.com/layumi/Person_reID_baseline_pytorch
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/leanderme/ConvNeXt-Tensorflow
https://github.com/leaplabthu/efficienttrain
https://github.com/leeBooMla/ICSR
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/leebebeto/pytorch-implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leeboomla/calr
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/leehomyc/mixup_pytorch
https://github.com/leelxh/Adaptive-Texture-Filtering-for-Single-Domain-Generalized-Segmentation
Adaptive Texture Filtering for Single-Domain Generalized Segmentation
https://github.com/leemathew1998/GradientWeight
https://github.com/leemathew1998/RG
https://github.com/leemsaebom/attention-guided-cam-visual-explanations-of-vision-transformer-guided-by-self-attention
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/leenaali1114/Hierarchical-Image-Clustering---Unsupervised-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leob03/E2E_Object_Pose_Estimator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/beit
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/caformer
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/convnext
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/volo
https://github.com/leongatys/DeepTextures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/leongatys/PytorchNeuralStyleTransfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lewis-morris/image_augment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/lfhase/feat
https://github.com/lhoyer/DAFormer
https://github.com/lhoyer/mic
https://github.com/libreai/neural-painters-x
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lightly-ai/lightly
https://github.com/linainsaf/RESNET50_PNEUMONIA_DETECTION
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.