https://github.com/alsgkals2/CoReD_Released
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
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/alsgkals2/cored
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
https://github.com/alvarobartt/serving-pytorch-models
https://github.com/amamov/vgg16-vs-resnet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aman-garg0001/fruit-classifier
https://github.com/amazon-research/long-short-term-transformer
https://github.com/amazon-science/long-short-term-transformer
https://github.com/ambityga/memsem
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ambujojha/SemiSupervisedLearning
Strong Baselines for Neural Semi-supervised Learning under Domain Shift
https://github.com/amdegroot/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amerch/CIFAR100-Training
https://github.com/amirdy/dog-breed-classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/amogh7joshi/engagement-detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/fer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/amogh7joshi/plant-health-detection
https://github.com/amzn/convolutional-handwriting-gan
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
https://github.com/andersoncarlosfs/image_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/andreasveit/densenet-pytorch
https://github.com/andrijdavid/clinical-brain-interface-2020
https://github.com/andy-yangz/writing_style_transfer
Data Selection Strategies for Multi-Domain Sentiment Analysis
https://github.com/andychinka/dcase-challenge
https://github.com/angelvillar96/FaceEmoji
https://github.com/angienikki/openwusu
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
https://github.com/anglihust/Unsupervised-vessel-segmentation
https://github.com/anguyen8/sam
https://github.com/anibali/dsnt-pose2d
https://github.com/aniket-patra-1998/FLOWER_RECOGNITION
https://github.com/aniquetahir/jax_ood
https://github.com/anjandeepsahni/face_classification
https://github.com/anjandeepsahni/face_recognition
https://github.com/anjith2006/bob.paper.mcae.icb2019
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
https://github.com/ankit-vaghela30/Google-landmark-prediction
https://github.com/annstrange/breast-cancer-cnn
https://github.com/anoushkrit/Knowledge
https://github.com/anthonysicilia/equitable-dialogue-acl2023
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error
https://github.com/anthonysicilia/leather-aacl2022
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.