  One-class classification (OCC) aims to learn an effective data description to
enclose all normal training samples and detect anomalies based on the deviation
from the data description. Current state-of-the-art OCC models learn a compact
normality description by hyper-sphere minimisation, but they often suffer from
overfitting the training data, especially when the training set is small or
contaminated with anomalous samples. To address this issue, we introduce the
interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a
one-class Gaussian anomaly classifier trained with adversarially interpolated
training samples. The Gaussian anomaly classifier differentiates the training
samples based on their distance to the Gaussian centre and the standard
deviation of these distances, offering the model a discriminability w.r.t. the
given samples during training. The adversarial interpolation is enforced to
consistently learn a smooth Gaussian descriptor, even when the training data is
small or contaminated with anomalous samples. This enables our model to learn
the data description based on the representative normal samples rather than
fringe or anomalous samples, resulting in significantly improved normality
description. In extensive experiments on diverse popular benchmarks, including
MNIST, Fashion MNIST, CIFAR10, MVTec AD and two medical datasets, IGD achieves
better detection accuracy than current state-of-the-art models. IGD also shows
better robustness in problems with small or contaminated training sets. Code is
available at https://github.com/tianyu0207/IGD.
