增加论文链接

增加论文链接

Author: Kedreamix

Parper-link/README.md

@@ -0,0 +1,113 @@
+# ClassicNetwork 图像分类网络论文链接汇总
+
+Classical network implemented by pytorch
+
+**LeNet:**
+
+- **LeNet:** LeNet-5, convolutional neural networks
+
+  [http://yann.lecun.com/exdb/lenet/index.html](http://yann.lecun.com/exdb/lenet/index.html)
+
+**AlexNet:**
+
+- ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, 2012
+
+  [http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
+
+**VGG:**
+
+- Very Deep Convolutional Networks for Large-Scale Image Recognition,Karen Simonyan,2014
+
+  [https://arxiv.org/abs/1409.1556](https://arxiv.org/abs/1409.1556)
+
+**ResNet:**
+
+- Deep Residual Learning for Image Recognition, He-Kaiming, 2016
+
+  [https://arxiv.org/abs/1512.03385](https://arxiv.org/abs/1512.03385)
+
+**Batch Normalization**
+
+- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,2015
+
+  [https://arxiv.org/abs/1502.03167](https://arxiv.org/abs/1502.03167)
+
+**ZFNet**
+
+- Visualizing and Understanding Convolutional Networks,2013
+
+  [https://arxiv.org/abs/1311.2901](https://arxiv.org/abs/1311.2901)
+
+**Inception系列**
+
+- **InceptionV1:** Going deeper with convolutions , Christian Szegedy , 2014
+
+  [https://arxiv.org/abs/1409.4842](https://arxiv.org/abs/1409.4842)
+
+- **InceptionV2 and InceptionV3:** Rethinking the Inception Architecture for Computer Vision , Christian Szegedy ,2015
+
+  [https://arxiv.org/abs/1512.00567](https://arxiv.org/abs/1512.00567)
+
+- **InceptionV4 and Inception-ResNet:** Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , Christian Szegedy ,2016
+
+  [https://arxiv.org/abs/1602.07261](https://arxiv.org/abs/1602.07261)
+
+**DenseNet:**
+
+- Densely Connected Convolutional Networks, 2017
+
+  [https://arxiv.org/abs/1608.06993](https://arxiv.org/abs/1608.06993)
+
+**ResNeXt:**
+
+- Aggregated Residual Transformations for Deep Neural Networks,2017
+
+  [https://arxiv.org/abs/1611.05431](https://arxiv.org/abs/1611.05431)
+
+**NASNet:**
+
+- Learning Transferable Architectures for Scalable Image Recognition
+
+  [https://arxiv.org/abs/1707.07012](https://arxiv.org/abs/1707.07012)
+
+**SENet**
+
+- Squeeze-and-Excitation Networks
+
+  [https://arxiv.org/abs/1709.01507](https://arxiv.org/abs/1709.01507)
+
+**MobileNet:**
+
+- **MobileNet(v1)** : MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
+
+  [https://arxiv.org/abs/1704.04861](https://arxiv.org/abs/1704.04861)
+
+- **MobileNet(v2)** : MobileNetV2: Inverted Residuals and Linear Bottlenecks
+
+  [https://arxiv.org/abs/1801.04381](https://arxiv.org/abs/1801.04381)
+
+- **MobileNet(v3)** : Searching for MobileNetV3
+
+  [https://arxiv.org/abs/1905.02244](https://arxiv.org/abs/1905.02244)
+
+
+
+**ShuffleNet:**
+
+- **ShuffleNet(v1)** :ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
+
+  [https://arxiv.org/abs/1707.01083](https://arxiv.org/abs/1707.01083)
+
+- **ShuffleNet(v2)** :ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
+
+  [https://arxiv.org/abs/1807.11164](https://arxiv.org/abs/1807.11164)
+
+**EfficientNet:**
+
+- EfficientNet(v1) :EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
+
+  [https://arxiv.org/abs/1905.11946](https://arxiv.org/abs/1905.11946)
+
+- EfficientNet(v2) :EfficientNetV2: Smaller Models and Faster Training
+
+  [https://arxiv.org/abs/2104.00298](https://arxiv.org/abs/2104.00298)