图 书 馆
XNOR网：使用二元卷积神经网络的图像网分类XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks |
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| 课程网址: | http://videolectures.net/eccv2016_rastegari_neural_networks/ |
| 主讲教师: | Mohammad Rastegari |
| 开课单位: | 艾伦人工智能研究所 |
| 开课时间: | 2016-10-24 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58x faster convolutional operations and 32x memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure). We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy. |
| 关 键 词: | 二进制操作; 网络二值化方法; 标准卷积神经网络 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-11-11：chenjy |
| 最后编审: | 2022-11-11：chenjy |
| 阅读次数: | 37 |