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.

本研究提出了一种机制来计算复杂模块化深度网络如何处理其输入的草图（简洁摘要）, 这些草图对输入和输出的基本信息进行总结和归纳，可以用于快速识别关键组件和摘要统计信息，草图是递归的，可以展开，捕捉复杂 DAG 结构的子组件等，同时也可以在仓库中组织草图以隐式形成知识图。最后，本文还表明，通过这些草图增加输入 / 输出对可以 theoretically 让我们更容易地学习地面实况深度网络。