https://github.com/VinayBhupalam/predicting_Pneumonia
Aggregated Residual Transformations for Deep 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/VisionLearningGroup/DA_Detection
Strong-Weak Distribution Alignment for Adaptive Object Detection
https://github.com/VisionLearningGroup/SSDA_MME
https://github.com/VisionLearningGroup/visda21-dev
https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet
https://github.com/Vivswan/ECE-3195-Course-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/WARM-3D/WARM-3D
https://github.com/Westlake-AI/MogaNet
https://github.com/Westlake-AI/openmixup
https://github.com/WinChua/CDRTR
https://github.com/WonJunPark/Efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/WuChenshen/MeRGAN
https://github.com/WuRui-Ella/Myfirststore
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/XavierJiezou/KTDA
Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation
https://github.com/Ximilar-com/tf-image
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/XingruiWang/superclevr-3D-question
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
https://github.com/Xnsam/clothing_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/YBZh/SymNets
Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
https://github.com/YaCpotato/B4ResearchDeepaugment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/YaCpotato/deepaugmentFix
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/YanYan0716/vision_transform
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Yang-Q-D/mindsporeCode/tree/master/ibnnet
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/YangYongJin/APEX
https://github.com/Yangget/Mixup_All-use
https://github.com/Yashgh7076/CPSC-8810-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/YimingXiao98/DamageCAT
https://github.com/YooJiHyeong/CCSA_PyTorch
Unified Deep Supervised Domain Adaptation and Generalization
https://github.com/YousefGamal220/Vision-Transformers
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/Yu-Fangxu/DARec
DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns
https://github.com/YudeWang/UNet-Satellite-Image-Segmentation
https://github.com/YutingLi0606/SURE
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/ZHKKKe/PixelSSL
https://github.com/ZHZisZZ/emulated-disalignment
https://github.com/ZTao-z/multiflow-resnet-ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZacharyGoshen/cifar-10-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zachdr1/CNNs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.