An important property for deep neural networks is the ability to perform robust out-of-distribution detection on previously unseen data. This property is essential for safety purposes when deploying models for real world applications. Recent studies show that probabilistic generative models can perform poorly on this task, which is surprising given that they seek to estimate the likelihood of training data. To alleviate this issue, we propose the exponentially tilted Gaussian prior distribution for the Variational Autoencoder (VAE) which pulls points onto the surface of a hyper-sphere in latent space. This achieves state-of-the art results on the area under the curve-receiver operator characteristics metric using just the negative log-likelihood that the VAE naturally assigns. Because this prior is a simple modification of the traditional VAE prior, it is faster and easier to implement than competitive methods.
翻译:深神经网络的一个重要属性是能够对先前不见的数据进行强力的分布外探测。 在为真实世界应用部署模型时,这种属性对于安全目的至关重要。最近的研究显示,概率型基因模型在这项任务上可能效果不佳,这是令人惊讶的,因为它们试图估计培训数据的可能性。为了缓解这一问题,我们建议对变形自动电解器(VAE)进行指数式倾斜的先前分布,该分布将点拉到潜层高孔表面。这在曲线-接收器操作员特性衡量标准下,仅使用VAE自然分配的负日志相似性,取得了该地区的最新艺术结果。由于这是对传统的VAE(VAE)之前的简单修改,因此比竞争性方法更快、更容易实施。