【论文笔记】Active Convolution: Learning the Shape of Convolution for Image Classification
Active Convolution: Learning the Shape of Convolution for Image Classification 主要思想 这篇论文的主要思想是在卷积神经网络中的卷积核的改进。论文提出近年来涌现出的网络，例如ResNet, Inception,都是对卷积神经网络结构的调整，提高网络的性能。卷积作为卷积神经网络的核心部分，改进的方法却不多。论文提出了一种可
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相关文章
- 1. 论文笔记《Active Convolution: Learning the Shape of Convolution for Image Classification》
- 2. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 论文笔记
- 3. 【Active Learning - 03】Adaptive Active Learning for Image Classification
- 4. Rethinking Atrous Convolution for Semantic Image Segmentation论文解
- 5. 【论文阅读笔记】Classification of ECG signals based on 1D convolution neural network
- 6. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (DeepLab v3+) 论文笔记
- 7. Scale-wise Convolution for Image Restoration论文理解
- 8. 《Understanding Convolution for Semantic Segmentation》论文笔记
- 9. 《Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation》论文笔记
- 10. 《Rethinking Atrous Convolution for Semantic Image Segmentation》论文笔记