Modern image files are usually progressively transmitted and provide a preview before downloading the entire image for improved user experience to cope with a slow network connection. In this paper, with a similar goal, we propose a progressive transmission framework for deep learning models, especially to deal with the scenario where pre-trained deep learning models are transmitted from servers and executed at user devices (e.g., web browser or mobile). Our progressive transmission allows inferring approximate models in the middle of file delivery, and quickly provide an acceptable intermediate outputs. On the server-side, a deep learning model is divided and progressively transmitted to the user devices. Then, the divided pieces are progressively concatenated to construct approximate models on user devices. Experiments show that our method is computationally efficient without increasing the model size and total transmission time while preserving the model accuracy. We further demonstrate that our method can improve the user experience by providing the approximate models especially in a slow connection.
翻译:现代图像文件通常会逐步传输,并在下载整个图像前提供预览,以便改进用户经验,应对缓慢的网络连接。在本文中,我们提出一个用于深层次学习模型的渐进传输框架,其目标相似,特别是为了处理事先经过训练的深层次学习模型从服务器传输并在用户设备(例如网络浏览器或移动设备)上执行的情景。我们的渐进传输允许在文件交付中推导大致模型,并快速提供可接受的中间输出。在服务器方面,一个深层学习模型被分割,并逐步传送到用户设备。然后,分离的碎片被逐渐融合到用户设备上构建近似模型。实验表明,我们的方法在不增加模型大小和总传输时间的同时,在保持模型准确性的同时,可以计算有效。我们进一步证明,我们的方法可以改进用户的经验,提供近似模型,特别是在缓慢的连接中。