Anomaly detection is the task of recognising novel samples which deviate significantly from pre-established normality. Abnormal classes are not present during training meaning that models must learn effective representations solely across normal class data samples. Deep Autoencoders (AE) have been widely used for anomaly detection tasks, but suffer from overfitting to a null identity function. To address this problem, we implement a training scheme applied to a Denoising Autoencoder (DAE) which introduces an efficient method of producing Adversarially Learned Continuous Noise (ALCN) to maximally globally corrupt the input prior to denoising. Prior methods have applied similar approaches of adversarial training to increase the robustness of DAE, however they exhibit limitations such as slow inference speed reducing their real-world applicability or producing generalised obfuscation which is more trivial to denoise. We show through rigorous evaluation that our ALCN method of regularisation during training improves AUC performance during inference while remaining efficient over both classical, leave-one-out novelty detection tasks with the variations-: 9 (normal) vs. 1 (abnormal) & 1 (normal) vs. 9 (abnormal); MNIST - AUCavg: 0.890 & 0.989, CIFAR-10 - AUCavg: 0.670 & 0.742, in addition to challenging real-world anomaly detection tasks: industrial inspection (MVTEC-AD - AUCavg: 0.780) and plant disease detection (Plant Village - AUC: 0.770) when compared to prior approaches
