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/ThomasWestfechtel/BIWAA
https://github.com/ThomasWestfechtel/GSDE
Gradual Source Domain Expansion for Unsupervised Domain Adaptation
https://github.com/TianshuXie/Cut-Thumbnail
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/TillBeemelmanns/auto-augment-tf2-operations
https://github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learning
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/Tools4Project/4501Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/TotalVariation/Flattenet
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/TowardHumanizedInteraction/TripletFace
https://github.com/TravisLeeTS/grabcvchallenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/TrinhQuocNguyen/CORE-ReID
https://github.com/TrinhQuocNguyen/CORE-ReID-v2
https://github.com/Trustworthy-AI-Group/TransferAttack
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
https://github.com/Trustworthy-ML-Lab/Interpretability-Guided-Defense
Efficient and Effective Augmentation Strategy for Adversarial Training
https://github.com/TuSimple/resnet.mxnet
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/UCB-ICSI-Vision-Group/decaf-release
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/UCBAIR/decaf-release
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/UKPLab/coling18-multimodalSurvey
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/UKPLab/useb
https://github.com/UdbhavPrasad072300/Transformer-Implementation
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/UdbhavPrasad072300/Transformer-Implementations
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Ugenteraan/Masked-AutoEncoder-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Ugenteraan/Vanilla-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/UnofficialJuliaMirror/DeepMark-deepmark
https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark
https://github.com/VLTSeg/VLTSeg
https://github.com/VachanVY/gpt.jax
https://github.com/Vaishnvi/ILLUME
https://github.com/Vakihito/SentimentYoutube
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Vamshi399/CarND-Vehicle-Detection
https://github.com/VamsiKrishna1211/gender_age-detection
https://github.com/VhalPurohit/290s
https://github.com/Vill-Lab/2024-TIP-MetaPrompt
https://github.com/VinayBN8997/ResNet-CIFAR10
https://github.com/VinayBhupalam/melanoma-detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.