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用于改进密集预测的平滑扩张卷积Smoothed Dilated Convolutions for Improved Dense Prediction |
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| 课程网址: | http://videolectures.net/kdd2018_wang_improved_dense_prediction/ |
| 主讲教师: | Zhengyang Wang |
| 开课单位: | 华盛顿州立大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Dilated convolutions, also known as atrous convolutions, have been widely explored in deep convolutional neural networks (DCNNs) for various tasks like semantic image segmentation, object detection, audio generation, video modeling, and machine translation. However, dilated convolutions suffer from the gridding artifacts, which hampers the performance of DCNNs with dilated convolutions. In this work, we propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions. Unlike existing models, which explore solutions by focusing on a block of cascaded dilated convolutional layers, our methods address the gridding artifacts by smoothing the dilated convolution itself. By analyzing them in both the original operation and the decomposition views, we further point out that the two degridding approaches are intrinsically related and define separable and shared (SS) operations, which generalize the proposed methods. We evaluate our methods thoroughly on two datasets and visualize the smoothing effect through effective receptive field analysis. Experimental results show that our methods yield significant and consistent improvements on the performance of DCNNs with dilated convolutions, while adding negligible amounts of extra training parameters. |
| 关 键 词: | 扩展卷积; 神经网络; 训练参数 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-07：chenjy |
| 最后编审: | 2022-12-07：chenjy |
| 阅读次数: | 47 |