https://github.com/youwenjing/reid_mgn-dgnet
Joint Discriminative and Generative Learning for Person Re-identification
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/yqyao/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ys-zong/medfair
https://github.com/ytx21cn/CDCL-human-seg
Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation
https://github.com/yuan-zm/dgt-st
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/yuchenlin/CDMA-NER
Neural Adaptation Layers for Cross-domain Named Entity Recognition
https://github.com/yudhisteer/Face-Recognition-with-Masks
https://github.com/yueatsprograms/ttt_cifar_release
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/yueatsprograms/ttt_imagenet_release
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/yui-mhcp/base_dl_project
https://github.com/yuleiqin/capro
https://github.com/yuleiqin/fopro
https://github.com/yunjey/domain-transfer-network
https://github.com/yuqizhu/covid19-classification
Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation
https://github.com/yuvrajarora/CSCI5922_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yuz1wan/video_distillation
https://github.com/yw0nam/DenseNet
https://github.com/yxgeee/MMT-plus
Improved Mutual Mean-Teaching for Unsupervised Domain Adaptive Re-ID
https://github.com/yyhappier/shortcutsuite
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
https://github.com/zangzelin/code_san_share
https://github.com/zchuning/repo
RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
https://github.com/zer0sh0t/artificial_intelligence/tree/master/vision_models/vision_transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/zhanghang1989/fast-autoaug-torch
https://github.com/zhangmin4215/PyTorch_Speaker_Verification
https://github.com/zhangq327/u-mae
https://github.com/zhangxinyu-xyz/PAST-ReID
Self-training with progressive augmentation for unsupervised cross-domain person re-identification
https://github.com/zhangxuhuizju/TCA
Learning Domain-Invariant Subspace using Domain Features and Independence Maximization
https://github.com/zhaochen0110/lmlm
https://github.com/zhaoqyu/ResNeXt-MGE
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/zheng-ningxin/Pruning-from-scratch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/zhjscut/IterLNL
https://github.com/zhongliangguo/acdm
Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization
https://github.com/zhu-xlab/softcon
https://github.com/zibbini/convnext-v2_tensorflow
https://github.com/zinengtang/tvlt
https://github.com/zitongyu/flex-modal-fas
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.