论文标题
通过可靠的不确定性估计来检测分布式样品
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
论文作者
论文摘要
变分自动编码器（VAE）是具有深层神经网络体系结构和贝叶斯方法的丰富表示能力的影响力模型。但是，VAE模型的弱点比分发（ID）输入分配（OOD）输入的可能性更高。为了解决这个问题，可靠的不确定性估计被认为对于对OOD输入的深入了解至关重要。在这项研究中，我们提出了改进的噪声对比先验（INCP），以便能够整合到VAE的编码中，称为Incpvae。 Incp可扩展，可训练且与VAE兼容，并且还采用了INCP的优点以进行不确定性估计。各种数据集上的实验表明，与标准VAE相比，我们的模型在OOD数据的不确定性估计方面表现出色，并且在异常检测任务中是可靠的。 Incpvae模型获得了OOD输入的可靠不确定性估计，并解决了VAE模型中的OOD问题。
Variational autoencoders (VAEs) are influential generative models with rich representation capabilities from the deep neural network architecture and Bayesian method. However, VAE models have a weakness that assign a higher likelihood to out-of-distribution (OOD) inputs than in-distribution (ID) inputs. To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs. In this study, we propose an improved noise contrastive prior (INCP) to be able to integrate into the encoder of VAEs, called INCPVAE. INCP is scalable, trainable and compatible with VAEs, and it also adopts the merits from the INCP for uncertainty estimation. Experiments on various datasets demonstrate that compared to the standard VAEs, our model is superior in uncertainty estimation for the OOD data and is robust in anomaly detection tasks. The INCPVAE model obtains reliable uncertainty estimation for OOD inputs and solves the OOD problem in VAE models.
