论文标题
旋转的环，径向和深度明智的可分离径向卷积
Rotated Ring, Radial and Depth Wise Separable Radial Convolutions
论文作者
论文摘要
简单的图像旋转显着降低了深神经网络的准确性。此外，所有可能的轮换训练会增加数据集，这也增加了训练持续时间。在这项工作中，我们解决了可训练的旋转不变卷积以及网的结构，因为完全连接的层只能与一维输入相关。一方面，我们表明我们的方法是不同模型和不同公共数据集的旋转不变的。我们还讨论了纯粹的旋转不变特征对准确性的影响。这项工作中提出的旋转自适应卷积模型比正常卷积模型更强化。因此，我们还提出了径向卷积的深度明智的可分离方法。链接到CUDA代码https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd4444e1a135/
Simple image rotations significantly reduce the accuracy of deep neural networks. Moreover, training with all possible rotations increases the data set, which also increases the training duration. In this work, we address trainable rotation invariant convolutions as well as the construction of nets, since fully connected layers can only be rotation invariant with a one-dimensional input. On the one hand, we show that our approach is rotationally invariant for different models and on different public data sets. We also discuss the influence of purely rotational invariant features on accuracy. The rotationally adaptive convolution models presented in this work are more computationally intensive than normal convolution models. Therefore, we also present a depth wise separable approach with radial convolution. Link to CUDA code https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/
