Abstract
We present a mechanism to compute a sketch (succinct summary) of how a complex modular deep network processes its inputs. The sketch summarizes essential information about the inputs and outputs of the network and can be used to quickly identify key components and summary statistics of the inputs. Furthermore, the sketch is recursive and can be unrolled to identify sub-components of these components and so forth, capturing a potentially complicated DAG structure. These sketches erase gracefully; even if we erase a fraction of the sketch at random, the remainder still retains the `high-weight' information present in the original sketch. The sketches can also be organized in a repository to implicitly form a `knowledge graph'; it is possible to quickly retrieve sketches in the repository that are related to a sketch of interest; arranged in this fashion, the sketches can also be used to learn emerging concepts by looking for new clusters in sketch space. Finally, in the scenario where we want to learn a ground truth deep network, we show that augmenting input/output pairs with these sketches can theoretically make it easier to do so.
Abstract (translated)
我们提出了一种机制来计算一个复杂的模块化深层网络如何处理其输入的草图（简明摘要）。该草图总结了有关网络输入和输出的基本信息，可用于快速识别关键组件和汇总输入统计数据。此外，草图是递归的，可以展开以识别这些组件的子组件等等，从而捕获可能复杂的DAG结构。这些草图可以很好地擦除；即使我们随机删除草图的一部分，其余部分仍然保留原始草图中存在的“高权重”信息。草图也可以在存储库中组织以隐式地形成“知识图”；可以在存储库中快速检索与感兴趣的草图相关的草图；以这种方式排列的草图还可以通过在草图空间中查找新的簇来学习新出现的概念。最后，在我们想要学习一个地面真相深层网络的场景中，我们展示了用这些草图增加输入/输出对在理论上可以更容易地做到这一点。
URL
https://arxiv.org/abs/1905.12730
