This paper proposes a methodology for discovering meaningful properties in data by exploring the latent space of unsupervised deep generative models. We combine manipulation of individual latent variables to extreme values outside the training range with methods inspired by causal inference into an approach we call causal disentanglement with extreme values (CDEV) and show that this approach yields insights for model interpretability. Using this technique, we can infer what properties of unknown data the model encodes as meaningful. We apply the methodology to test what is meaningful in the communication system of sperm whales, one of the most intriguing and understudied animal communication systems. We train a network that has been shown to learn meaningful representations of speech and test whether we can leverage such unsupervised learning to decipher the properties of another vocal communication system for which we have no ground truth. The proposed technique suggests that sperm whales encode information using the number of clicks in a sequence, the regularity of their timing, and audio properties such as the spectral mean and the acoustic regularity of the sequences. Some of these findings are consistent with existing hypotheses, while others are proposed for the first time. We also argue that our models uncover rules that govern the structure of communication units in the sperm whale communication system and apply them while generating innovative data not shown during training. This paper suggests that an interpretation of the outputs of deep neural networks with causal methodology can be a viable strategy for approaching data about which little is known and presents another case of how deep learning can limit the hypothesis space. Finally, the proposed approach combining latent space manipulation and causal inference can be extended to other architectures and arbitrary datasets.

翻译：本文提出了一种方法论，通过探索无监督深度生成模型的潜在空间来发现数据中有意义的属性。我们将单个潜在变量操控至训练范围外的极端值，与受因果推断启发的方法相结合，提出了一种称为“极端值因果解缠”（CDEV）的方法，并证明该方法能够为模型可解释性提供洞见。利用这一技术，我们可以推断模型将未知数据中的哪些属性编码为有意义的特征。我们将该方法应用于测试抹香鲸通信系统中有意义的内容——这是最引人入胜且研究不足的动物通信系统之一。我们训练了一个已被证明能学习语音有意义表征的网络，并检验是否可以利用这种无监督学习解码另一类缺乏真实标注的语音通信系统的属性。所提出的技术表明，抹香鲸通过序列中的咔嗒声次数、时间规律性以及音频属性（如频谱均值和序列声学规律性）来编码信息。其中部分发现与现有假说一致，而另一些则属首次提出。我们还论证了模型能够揭示抹香鲸通信系统中通信单元结构的规则，并在生成训练中未出现的新数据时应用这些规则。本文表明，结合因果方法论解释深度神经网络输出，可成为逼近未知数据的一种可行策略，并展示了深度学习如何限制假设空间的又一案例。最后，所提出的潜在空间操纵与因果推断相结合的方法可推广至其他架构和任意数据集。