【論文筆記】Active Convolution: Learning the Shape of Convolution for Image Classification
Active Convolution: Learning the Shape of Convolution for Image Classification 主要思想 這篇論文的主要思想是在卷積神經網絡中的卷積核的改進。論文提出近年來涌現出的網絡，例如ResNet, Inception,都是對卷積神經網絡結構的調整，提高網絡的性能。卷積作爲卷積神經網絡的核心部分，改進的方法卻不多。論文提出了一種可
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相關文章
- 1. 論文筆記《Active Convolution: Learning the Shape of Convolution for Image Classification》
- 2. 【Active Learning - 01】 2013_CVPR_Adaptive Active Learning for Image Classification 論文筆記
- 3. 【Active Learning - 03】Adaptive Active Learning for Image Classification
- 4. Rethinking Atrous Convolution for Semantic Image Segmentation論文解
- 5. 【論文閱讀筆記】Classification of ECG signals based on 1D convolution neural network
- 6. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (DeepLab v3+) 論文筆記
- 7. Scale-wise Convolution for Image Restoration論文理解
- 8. 《Understanding Convolution for Semantic Segmentation》論文筆記
- 9. 《Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation》論文筆記
- 10. 《Rethinking Atrous Convolution for Semantic Image Segmentation》論文筆記