https://github.com/hollance/VGGNet-Metal
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Domain Adaptation
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- 평가표
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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/holstgr-kaust/keras-tutorials
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hongkunsun/paratranscnn
https://github.com/hongxin001/JoCoR
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation
https://github.com/hongxin001/ODNL
GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation
https://github.com/hongyi-zhang/Fixup
https://github.com/hongyi-zhang/mixup
https://github.com/horse007666/ResNet
https://github.com/houstonsantos/CassavaLeafDisease
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hpprc/simple-simcse-ja
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
https://github.com/hsd1503/resnet1d
https://github.com/hsinyilin19/resnetvae
https://github.com/hthoai/UI2IT
https://github.com/hu-my/taskattributedistance
https://github.com/huawei-noah/kd-nlp
https://github.com/huitangtang/ViCatDA
https://github.com/hukefei/deep-person-reid-master
Learning Generalisable Omni-Scale Representations for Person Re-Identification
https://github.com/hun1204/DeepLearning_Visualization_Basic
https://github.com/hunto/dist_kd
https://github.com/hustic/TinyImageSet
https://github.com/hustvl/sparseinst
https://github.com/huynhtuan17ti/AI4VN-Hackathon2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/huyvd7/huyvd7
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
https://github.com/huzpsb/EffTransfer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hwk0702/keras2torch/tree/main/Computer_Vision/Model_Soup
https://github.com/hx19940102/Image-Segmentation-based-on-FCN
https://github.com/hyang0129/foodclassapp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/hycis/TensorGraph
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hyeonseobnam/sagnet
https://github.com/hyeonseobnam/style-agnostic-networks
https://github.com/hysts/pytorch_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
https://github.com/hytseng0509/DRIT_hr
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/hyunwoongko/resnext-parallel
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/hz2538/iot_JennyGo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.