Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 13
- 평가표
- 58
- 보존된 평가 기록
- 391
Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mahmoodlab/hipt
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/makatx/YOLO_ResNet
https://github.com/makeyourownmaker/mixup
https://github.com/malicd/LiSu
LiSu: A Dataset and Method for LiDAR Surface Normal Estimation
https://github.com/malin9402/2020-0221
https://github.com/manankshastri/ART
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mancinimassimiliano/latent_domains_DA
https://github.com/mancinimassimiliano/pytorch_wbn
https://github.com/manoranjan03/PaperImplementations
https://github.com/map-learning/tl2la
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/maragori/DeepfakeForensics-v1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marcointrovigne/WeatherDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marload/ConvNets-TensorFlow2
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/marrlab/dinobloom
https://github.com/martinsbruveris/tensorflow-image-models
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/mashaan14/ADDA
https://github.com/mashaan14/ADDA-toy
https://github.com/mashaan14/DANN-toy
https://github.com/mashaan14/SENTRY-toy
SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
https://github.com/mashaan14/VisionTransformer-MNIST
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/masoudnick/brain-tumor-mri-classification
https://github.com/matakshay/Neural_Image_Caption_Generator
https://github.com/mathiasunberath/DeepDRR
DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures
https://github.com/matsuolab/t3a
https://github.com/mauriceweiler/MobiusCNNs
https://github.com/maxiao0234/ttga
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
https://github.com/maxwelltsai/DeepGalaxy
https://github.com/mbsariyildiz/resnet-pytorch
https://github.com/mbuet2ner/local-global-features-cnn
https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat
https://github.com/mcd4874/neurips_competition
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/mdda/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mdmhriday/vision-transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/megvii-research/basecls/tree/main/zoo/public/effnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/megvii-research/basecls/tree/main/zoo/public/resnet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.