基于TensorFlow 2.x的一些CNN模块/网络的实现
开源一些基于TensorFlow 2.x的CNN模块/网络的实现，可能不定时更新。仓库链接：TensorFlow-2-Implementations-of-CNN-Based-Networks
目前的实现包括：
Feature Extraction/Fusion Blocks
Atrous Convolutional Block for 1D (data points / sequences) or 2D inputs (images / feature maps), suggested by An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Receptive Field Block, from Receptive Field Block Net for Accurate and Fast Object Detection
Attention Blocks
Squeeze-and-Excitation Block (Kind of Channel Attention), from Squeeze-and-Excitation Networks
Convolutional Block Attention Module (CBAM), including Channel Attention Module and Spatial Attention Module, from CBAM: Convolutional Block Attention Module
Non-Local Block, including ‘Gaussian’, ‘Embedded Gaussian’, ‘Dot Product’ and ‘Concatenation’ modes, from Non-local Neural Networks
Dual Attention Module, including Channel Attention Module and Position Attention Module, from Dual Attention Network for Scene Segmentation
Backbone Networks
参考了以下文章/仓库中的一些代码实现，在此感谢：
[1] https://github.com/philipperemy/keras-tcn
[2] https://github.com/Baichenjia/Tensorflow-TCN/blob/master/tcn.py
[3] https://arxiv.org/pdf/1803.01271.pdf
[4] https://arxiv.org/pdf/1711.07767.pdf
[5] https://arxiv.org/abs/1709.01507
[6] https://github.com/kobiso/CBAM-tensorflow-slim/blob/master/nets/attention_module.py
[7] https://arxiv.org/abs/1807.06521
[8] https://arxiv.org/pdf/1711.07971.pdf
[9] https://github.com/titu1994/keras-non-local-nets/blob/master/non_local.py
[10] https://github.com/Tramac/Non-local-tensorflow/tree/master/non_local
[11] https://arxiv.org/pdf/1809.02983.pdf
[12] https://github.com/niecongchong/DANet-keras/blob/master/layers/attention.py
[13] https://github.com/okason97/DenseNet-Tensorflow2/blob/master/densenet/densenet.py
[14] https://arxiv.org/pdf/1608.06993.pdf
基于TensorFlow 2.x的一些CNN模块/网络的实现
