Data are often accommodated on centralized storage servers. This is the case, for instance, in remote sensing and astronomy, where projects produce several petabytes of data every year. While machine learning models are often trained on relatively small subsets of the data, the inference phase typically requires transferring significant amounts of data between the servers and the clients. In many cases, the bandwidth available per user is limited, which then renders the data transfer to be one of the major bottlenecks. In this work, we propose a framework that automatically selects the relevant parts of the input data for a given neural network. The model as well as the associated selection masks are trained simultaneously such that a good model performance is achieved while only a minimal amount of data is selected. During the inference phase, only those parts of the data have to be transferred between the server and the client. We propose both instance-independent and instance-dependent selection masks. The former ones are the same for all instances to be transferred, whereas the latter ones allow for variable transfer sizes per instance. Our experiments show that it is often possible to significantly reduce the amount of data needed to be transferred without affecting the model quality much.
翻译:例如,在遥感和天文学方面,项目每年产生数个数据小字节。虽然机器学习模型往往在数据中相对较小的子集上接受培训,但推断阶段通常要求在服务器和客户之间转移大量数据。在许多情况下,每个用户可利用的带宽有限,因此数据传输成为主要瓶颈之一。在这项工作中,我们提议一个框架,为某个神经网络自动选择输入数据的相关部分。模型和相关的选择面罩同时培训,以便实现良好的模型性能,而只选择少量数据。在推断阶段,只有数据中的某些部分必须在服务器和客户之间传输。我们建议,每个用户之间的带宽是有限的,然后使数据传输成为主要的瓶颈之一。在所有这些情况下,前一个框架都是一样的,而后一个框架允许每个实例的传输大小不同。我们的实验表明,在不严重影响模型质量的情况下,通常有可能大大减少需要转移的数据数量。