Papers with Code 아카이브
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/k-miran/hear
https://github.com/kadmkbl/SURGEON
SURGEON: Memory-Adaptive Fully Test-Time Adaptation via Dynamic Activation Sparsity
https://github.com/kairess/efficientnet_example
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/kaist-dmlab/SELFIE
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kakaobrain/coyo-dataset
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/kakaobrain/irm-empirical-study
https://github.com/kakaoenterprise/Learning-Debiased-Disentangled
https://github.com/kamalkraj/Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/kamanphoebe/motiondetection
https://github.com/kanhereashwin/cs236-project
https://github.com/kaonashi-tyc/zi2zi
https://github.com/karpathy/build-nanogpt
https://github.com/karpathy/llm.c
https://github.com/kaseris/ILSVRCPlus
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/katoro8989/irm_variants_calibration
https://github.com/kboseong/RotNet
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/kbpi314/iclust_paper
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kclip/localized-adaptive-risk-control
https://github.com/kdha0727/lung-opacity-and-covid-chest-x-ray-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/keanson/revisit-logistic-softmax
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
https://github.com/keillernogueira/exploit-cnn-rs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kenomo/industrial-clip
https://github.com/keras-team/keras-applications
https://github.com/keras-team/keras-cv
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/keras-team/keras-cv/blob/master/keras_cv/models/vit.py
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/keras-team/keras-io/blob/master/examples/vision/image_classification_with_vision_transformer.py
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/keras-team/keras-io/blob/master/examples/vision/masked_image_modeling.py
https://github.com/keras-team/keras/blob/master/keras/applications/convnext.py
https://github.com/kevin851066/Deep_Learning_for_Computer_Vision
https://github.com/keytoyze/visionts
https://github.com/kgl-prml/Contrastive-Adaptation-Network-for-Unsupervised-Domain-Adaptation
Contrastive Adaptation Network for Unsupervised Domain Adaptation
https://github.com/khalilouardini/Cassiopeia_Transcriptome
https://github.com/khalilouardini/treeVAE-reproducibility
https://github.com/khanrc/pt.fractalnet
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/khawajatalhahaseeb/Class-Activation-Map-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.