  Anomaly detection is the task of recognising novel samples which deviate
significantly from pre-establishednormality. Abnormal classes are not present
during training meaning that models must learn effective rep-resentations
solely across normal class data samples. Deep Autoencoders (AE) have been
widely used foranomaly detection tasks, but suffer from overfitting to a null
identity function. To address this problem, weimplement a training scheme
applied to a Denoising Autoencoder (DAE) which introduces an efficient methodof
producing Adversarially Learned Continuous Noise (ALCN) to maximally globally
corrupt the input priorto denoising. Prior methods have applied similar
approaches of adversarial training to increase the robustnessof DAE, however
they exhibit limitations such as slow inference speed reducing their real-world
applicabilityor producing generalised obfuscation which is more trivial to
denoise. We show through rigorous evaluationthat our ALCN method of
regularisation during training improves AUC performance during inference
whileremaining 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.
