We present a method for conditional sampling with normalizing flows when only part of an observation is available. We rely on the following fact: if the flow's domain can be partitioned in such a way that the flow restrictions to subdomains keep the bijectivity property, a lower bound to the conditioning variable log-probability can be derived. Simulation from the variational conditional flow then amends to solving an equality constraint. Our contribution is three-fold: a) we provide detailed insights on the choice of variational distributions; b) we propose how to partition the input space of the flow to preserve bijectivity property; c) we propose a set of methods to optimise the variational distribution in specific cases. Through extensive experiments, we show that our sampling method can be applied with success to invertible residual networks for inference and classification.
翻译:我们提出了一个在只有部分观察数据时进行有条件抽样并实现流动正常化的方法。我们依靠以下事实:如果流动的域可以分割,使对子域的流量限制能够保持双向属性,那么可以得出与可变日志的可变性条件相对应的较低约束。从可变有条件流动中模拟,然后修正解决平等制约。我们的贡献有三重:a)我们提供关于可变分布选择的详细见解;b)我们建议如何分割流动的输入空间,以保存双向属性;c)我们提出一套方法,在特定情况下优化可变分布。我们通过广泛的实验,表明我们的抽样方法可以成功地应用于不可忽略的剩余网络,以便进行推断和分类。