Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of high-dimensional structures, and propagating gradients through the relevant structures often requires enumerating over an exponentially large latent space. Recently, various approaches were devised to propagate approximated gradients without enumerating over the space of possible structures. In this work, we use Natural Evolution Strategies (NES), a class of gradient-free black-box optimization algorithms, to learn discrete structured VAEs. The NES algorithms are computationally appealing as they estimate gradients with forward pass evaluations only, thus they do not require to propagate gradients through their discrete structures. We demonstrate empirically that optimizing discrete structured VAEs using NES is as effective as gradient-based approximations. Lastly, we prove NES converges for non-Lipschitz functions as appear in discrete structured VAEs.

本研究利用自然演化策略算法学习离散结构化变分自编码器（VAE），它们是一种能够在生成学习中表示语义潜在空间的方法，此算法不需要梯度传播，通过前向传递估计梯度；经实验证明，它优化离散结构化VAE的效果与基于梯度的算法相当，并证明了在很多情况下NES能够收敛于非Lipschitz函数。

使用自然进化策略学习离散结构变分自编码器