Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Double-Adversarial Activation Anomaly Detection: Adversarial
  Autoencoders are Anomaly Generators   Anomaly detection is a challenging task for machine learning algorithms due
to the inherent class imbalance. It is costly and time-demanding to manually
analyse the observed data, thus usually only few known anomalies if any are
available. Inspired by generative models and the analysis of the hidden
activations of neural networks, we introduce a novel unsupervised anomaly
detection method called DA3D. Here, we use adversarial autoencoders to generate
anomalous counterexamples based on the normal data only. These artificial
anomalies used during training allow the detection of real, yet unseen
anomalies. With our novel generative approach, we transform the unsupervised
task of anomaly detection to a supervised one, which is more tractable by
machine learning and especially deep learning methods. DA3D surpasses the
performance of state-of-the-art anomaly detection methods in a purely
data-driven way, where no domain knowledge is required.

28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga