https://github.com/lpirola13/flower_recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Papers with Code 아카이브
Domain Adaptation
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- 평가표
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- 보존된 평가 기록
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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/ls-da3m0ns/Short-ResNet
https://github.com/lsh110600/person_re_id
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/ltruncel/Microsoft_Azure_50daysofudacity
https://github.com/lucidrains/denoising-diffusion-pytorch
https://github.com/lucidrains/vit-pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lucylow/salty-wet-man
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ludics/ViT-Retri
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lukas-blecher/LaTeX-OCR
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/lukasbommes/pv-hawk
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning
https://github.com/lukemelas/EfficientNet-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lukemelas/PyTorch-Pretrained-ViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/luomingshuang/GE2E-SV-TI-Chinese-LMS
https://github.com/luomingshuang/GE2E-SV-TI-Timit-LMS
https://github.com/luomingshuang/GE2E-SV-TI-Voxceleb-LMS
https://github.com/luomingshuang/GE2E-SV-TI-thchs30-LMS
https://github.com/luuchung/cifar-100
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lvweiwolf/efficientdet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lxy000719/ResNet-50-pneumonia-classification
https://github.com/lyk19940625/MyRFBNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lym29/DASeg
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/lyqcom/convnext
https://github.com/lyqcom/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lywang12/cupi-domain
https://github.com/lywang12/cuti-domain
https://github.com/lyxxn0414/test-data-generation
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/lzx6/deep-coral
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
https://github.com/lzx6/pytorch_DA
https://github.com/ma7555/Augz
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/mabhay3420/Deep-Into-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/machinelearning-goettingen/EfficientNet-ModelScaling
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/maciejdomagala/DLWP_notes
https://github.com/mackitron/COVID19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/magicleap/SuperGluePretrainedNetwork
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahfujau/domain_adaptation_iccv15
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.