https://github.com/PaddlePaddle/PaddleOCR
Bag of Tricks for Image Classification with Convolutional Neural Networks
Papers with Code 아카이브
Domain Adaptation
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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/PaddlePaddle/PaddleSpeech
https://github.com/PaddlePaddle/models
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/PatrickHua/SimpleMAE
https://github.com/PatrickZH/Improved-Deep-Leakage-from-Gradients
https://github.com/Paul92/cp_2020
https://github.com/Pealing/DDAN
Dual Distribution Alignment Network for Generalizable Person Re-Identification
https://github.com/Pengxin-Guo/SPDA
https://github.com/PengyiZhang/MIADeepSSL
https://github.com/PistonY/ResidualAttentionNetwork
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/PolyArch/fp-diannao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PotatoSpudowski/CactiNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Proxim123/person-reID-No1-
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/PsorTheDoctor/microarray-data
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/Pulkit-Khandelwal/mldg
Learning to Generalize: Meta-Learning for Domain Generalization
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/QQBrowserVideoSearch/CBVS-UniCLIP
https://github.com/RAF96/ifmo-2019-deep-learning-coursework
https://github.com/RF5/CurveNet
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
https://github.com/RUCAIBox/LLMBox
https://github.com/Raghvender1205/ConvNeXt
https://github.com/RainbowNebula/PETS
Periodically Exchange Teacher-Student for Source-Free Object Detection
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/Raykoooo/IAST
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
https://github.com/RikoLi/PCL-CLIP
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/RobotMobile/cv-deep-learning-paper-review
https://github.com/Rohed/ml-1
https://github.com/RohitDhankar/kaggle_wheat
https://github.com/RudreshVeerkhare/StyleGan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Ruturaj123/VGG16-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Ryoo72/dimension-wise_mixup
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.