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Title: GitHub - dyfcalid/DeepLearning-CNN-Models: Deep learning representative convolutional neural networks under tensorflow 2 framework, trained on cifar-10 dataset.

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X Title: GitHub - dyfcalid/DeepLearning-CNN-Models: Deep learning representative convolutional neural networks under tensorflow 2 framework, trained on cifar-10 dataset.

Description: Deep learning representative convolutional neural networks under tensorflow 2 framework, trained on cifar-10 dataset. - dyfcalid/DeepLearning-CNN-Models

Open Graph Description: Deep learning representative convolutional neural networks under tensorflow 2 framework, trained on cifar-10 dataset. - dyfcalid/DeepLearning-CNN-Models

X Description: Deep learning representative convolutional neural networks under tensorflow 2 framework, trained on cifar-10 dataset. - dyfcalid/DeepLearning-CNN-Models

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https://patch-diff.githubusercontent.com/dyfcalid/DeepLearning-CNN-Models#papers
ImageNet Classification with Deep ConvolutionalNeural Networkshttp://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
Network In Networkhttps://arxiv.org/pdf/1312.4400.pdf
Very Deep Convolutional Networks for Large-Scale Image Recognitionhttps://arxiv.org/pdf/1409.1556
Going Deeper with Convolutionshttps://arxiv.org/pdf/1409.4842
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shifthttps://arxiv.org/pdf/1502.03167
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learninghttps://arxiv.org/pdf/1602.07261
Deep Residual Learning for Image Recognitionhttps://arxiv.org/pdf/1512.03385.pdf
Identity Mappings in Deep Residual Networkshttps://arxiv.org/pdf/1603.05027v3.pdf
Multi-Scale Context Aggregation by Dilated Convolutionshttps://arxiv.org/pdf/1511.07122
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizehttps://arxiv.org/pdf/1602.07360v3
Deep Networks with Stochastic Depthhttps://arxiv.org/pdf/1603.09382v1
Ultra-Deep Neural Networks without Residualshttps://arxiv.org/pdf/1605.07648.pdf
Xception: Deep Learning with Depthwise Separable Convolutionshttps://arxiv.org/pdf/1610.02357
Deep Pyramidal Residual Networkshttps://arxiv.org/pdf/1610.02915
Aggregated Residual Transformations for Deep Neural Networkshttps://arxiv.org/pdf/1611.05431
Wide Residual Networkshttps://arxiv.org/pdf/1605.07146
Densely Connected Convolutional Networkshttps://arxiv.org/pdf/1608.06993
Dual Path Networkshttps://arxiv.org/pdf/1707.01629
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Deviceshttps://arxiv.org/pdf/1707.01083
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designhttps://arxiv.org/pdf/1807.11164
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationshttps://arxiv.org/pdf/1704.04861
MobileNetV2: Inverted Residuals and Linear Bottleneckshttps://arxiv.org/pdf/1801.04381
Searching for MobileNetV3https://arxiv.org/pdf/1905.02244
Squeeze-and-Excitation Networkshttps://arxiv.org/pdf/1709.01507
CBAM: Convolutional Block Attention Modulehttps://arxiv.org/pdf/1807.06521
Selective Kernel Networkshttps://arxiv.org/pdf/1903.06586
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkshttps://arxiv.org/pdf/1905.11946
ResNeSt: Split-Attention Networkshttps://arxiv.org/pdf/2004.08955
Bag of Tricks for Image Classification with Convolutional Neural Networkshttps://arxiv.org/pdf/1812.01187
Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classificationhttps://arxiv.org/pdf/1806.01593
Learning Transferable Architectures for Scalable Image Recognitionhttps://arxiv.org/pdf/1707.07012
Regularized Evolution for Image Classifier Architecture Searchhttps://arxiv.org/pdf/1802.01548
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