Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/ssagawa/overparam_spur_corr
https://github.com/sshan-zhao/DG_via_ER
https://github.com/staghado/vit.cpp
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/stanford-oval/genienlp
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/stanl1y/med_TwoCycleLearning-and-MixUp
https://github.com/stardust-coder/japanese-lm-med-harness
JMedLoRA:Medical Domain Adaptation on Japanese Large Language Models using Instruction-tuning
https://github.com/starfoe/Eye-bnb
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/stefkim/stylegan-batik
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/stes/torch-associative
https://github.com/stevenwalton/scs-cct
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/stone5265/3c-reid
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/stu92054/Domain-adaptation-on-segmentation
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/sud0301/semisup-semseg
https://github.com/suhas1999/Flip-kart-grid-challenge
https://github.com/sumitkutty/Anti-Spoof-Face-Recognition
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunjungAn/Tensorflow2.x-CV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sunlizhuang/YOLOv1-PaddlePaddle
https://github.com/sunnysoni97/single_image_fl
https://github.com/sunshineatnoon/LinearStyleTransfer
Learning Linear Transformations for Fast Arbitrary Style Transfer
https://github.com/susomena/DeepSlowMotion
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/svdesai/heading-date-estimation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/swasun/VQ-VAE-images
https://github.com/swetak20/Deep_into_CNNs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/syiin/ann_cancer_detection
https://github.com/syiin/resnet_cancer_detection
https://github.com/syliz517/clip-reid
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/syscv/shift-dev
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation
https://github.com/szacho/augmix-tf
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/t0930198/backup
https://github.com/tachitachi/GradientReversal
https://github.com/tadeephuy/GradientReversal
https://github.com/taesungp/contrastive-unpaired-translation
https://github.com/tahmid0007/VisionTransformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/tajamul21/fate
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.