论文标题
深度：开源深度自我端到端的学习框架
deepSELF: An Open Source Deep Self End-to-End Learning Framework
论文作者
论文摘要
我们介绍了一个开源工具包，即深度自我端到端的学习框架（深度），作为多模式信号的深度自端学习框架的工具包。据我们所知，这是第一个组装一系列最先进的深度学习技术的公共工具包。拟议的深度工具包的亮点包括：首先，它可用于分析各种多模式信号，包括图像，音频和单个或多通道传感器数据。其次，我们提供了多种选择，例如通过傅立叶或小波转换来进行预处理，例如过滤或频谱图像。第三，可以定制大量拓扑，1D/2D/3D CNN和RNN/LSTM/GRU，并且可以轻松地使用一系列预处理的2D CNN型号，例如Alexnet，vggnet，vggnet，Resnet。最后但并非最不重要的一点是，除这些特征之上，深度自我不仅可以灵活地用作单个模型，而且可以用作融合。
We introduce an open-source toolkit, i.e., the deep Self End-to-end Learning Framework (deepSELF), as a toolkit of deep self end-to-end learning framework for multi-modal signals. To the best of our knowledge, it is the first public toolkit assembling a series of state-of-the-art deep learning technologies. Highlights of the proposed deepSELF toolkit include: First, it can be used to analyse a variety of multi-modal signals, including images, audio, and single or multi-channel sensor data. Second, we provide multiple options for pre-processing, e.g., filtering, or spectrum image generation by Fourier or wavelet transformation. Third, plenty of topologies in terms of NN, 1D/2D/3D CNN, and RNN/LSTM/GRU can be customised and a series of pretrained 2D CNN models, e.g., AlexNet, VGGNet, ResNet can be used easily. Last but not least, above these features, deepSELF can be flexibly used not only as a single model but also as a fusion of such.
