Abstract
Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-of-distribution (OOD) samples. To obtain a well-regularized latent space that is conducive for uncertainty estimation, existing methods bring in significant changes to model architectures and training procedures. In this paper, we present a lightweight, fast, and high-performance regularization method for Mahalanobis distance-based uncertainty prediction, and that requires minimal changes to the network's architecture. To derive Gaussian latent representation favourable for Mahalanobis Distance calculation, we introduce a self-supervised representation learning method that separates in-class representations into multiple Gaussians. Classes with non-Gaussian representations are automatically identified and dynamically clustered into multiple new classes that are approximately Gaussian. Evaluation on standard OOD benchmarks shows that our method achieves state-of-the-art results on OOD detection with minimal inference time, and is very competitive on predictive probability calibration. Finally, we show the applicability of our method to a real-life computer vision use case on microorganism classification.
Abstract (translated)
最近的工作表明，网络的隐态空间的数据分布对于估计分类不确定性和检测分布不符(OOD)样本非常有用。为了获得有利于不确定性估计的充分的Regularization，现有方法对模型架构和训练程序进行了重大更改。在本文中，我们提出了一种轻量级、快速且高性能的Regularization方法，以马氏距离为基础的不确定性预测，该方法只需要对网络架构进行微小的修改。为了推导马氏距离计算有利的高斯隐态表示，我们引入了一种自监督表示学习方法，将班级表示分离成多个高斯函数。具有非高斯表示的班级自动识别并动态地聚类成多个近似高斯的新班级。在标准OOD基准测试中，我们的方法在 inference 时间 minimal 的情况下实现了OOD检测的最先进的结果，并在预测概率校准方面非常具有竞争力。最后，我们展示了我们方法对微生物分类的实际计算机视觉应用案例的适用性。
URL
https://arxiv.org/abs/2305.13849
