Learning non-linear invariants for unsupervised out-of-distribution detection

An important hurdle to overcome before machine learning models can be reliably deployed in practice is identifying when samples are different from those seen during training, as the output for unexpected samples are often confidently incorrect, while not being identifiable as such. This problem is known as out-of-distribution (OOD) detection. A popular approach for the unsupervised OOD case is to reject samples with a high Mahalanobis distance with regards to the mean features of the training data. Recent work showed that the Mahalanobis distance can be thought of as finding the training data invariants, and rejecting OOD samples that violate them. A key limitation to this approach is that it is limited to linear relations only. Here, we present a novel method capable of identifying non-linear invariants in the data. These are learned using a reversible neural network, consisting of alternating rotation and coupling layers. Results on a varied number of tasks show it to be the best method overall, and achieving state-of-the-art results on some of the experiments

SS3D: Unsupervised Out-of-Distribution Detection and Localization for Medical Volumes

We present an extension of the self-supervised outlier detection (SSD) framework to the three-dimensional case. We first apply contrastive learning on a network using a general dataset of two-dimensional slices randomly sampled from all the available training data. This network serves as a latent embedding encoder of the input images. We model the in-distribution latent density as a multivariate Gaussian, fitted to the embeddings of the training slices. At test time, each test sample is scored by summing the Mahalanobis distances from all its slices to the means of the learned Gaussians. While mainly meant as a sample-level method, this approach additionally enables coarse localization, scoring each voxel by the minimum Mahalanobis distance among the slices that contain it. On the sample-level task of the 2021 MICCAI Medical Out-of-Distribution Analysis Challenge, our method ranked second on the challenging abdominal dataset, and fourth overall. Moreover, we show that with pretrained features and the right choice of architecture, a further boost in performance can be gained