Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence.
Recently, classification-based methods were shown to achieve superior results on this task.
In this work, we present a unifying view and propose an open-set method to relax current generalization assumptions.
Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations.
Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types.
The strong performance of our method is extensively validated on multiple datasets from different domains.
Detecting anomalies in perceived data is a key ability for humans and for artificial intelligence.
Humans often detect anomalies to give early indications of danger or to discover unique opportunities.
Anomaly detection systems are being used by artificial intelligence to discover credit card fraud, for detecting cyber intrusion, alert predictive maintenance of industrial equipment and for discovering attractive stock market opportunities.
The typical anomaly detection setting is a one class classification task, where the objective is to classify data as normal or anomalous.
The importance of the task stems from being able to raise an alarm when detecting a different pattern from those seen in the past, therefore triggering further inspection.
This is fundamentally different from supervised learning tasks, in which examples of all data classes are observed.
There are different possible scenarios for anomaly detection methods.
In supervised anomaly detection, we are given training examples of normal and anomalous patterns.
This scenario can be quite well specified, however obtaining such supervision may not be possible.
For example in cyber security settings, we will not have supervised examples of new, unknown computer viruses making supervised training difficult.
On the other extreme, fully unsupervised anomaly detection, obtains a stream of data containing normal and anomalous patterns and attempts to detect the anomalous data.
In this work we deal with the semi-supervised scenario.
In this setting, we have a training set of normal examples (which contains no anomalies).
After training the anomaly detector, we detect anomalies in the test data, containing both normal and anomalous examples.
This supervision is easy to obtain in many practical settings and is less difficult than the fully-unsupervised case.
Many anomaly detection methods have been proposed over the last few decades.
They can be broadly classified into reconstruction and statistically based methods.
Recently, deep learning methods based on classification have achieved superior results.
Most semi-supervised classificationbased methods directly attempt to solve anomaly detection, despite only having normal training data.
One example is: Deep-SVDD ) -one-class classification using a learned deep space.
Another type of classification-based methods is self-supervised i.e. methods that solve one or more classification-based auxiliary tasks on the normal training data, and this is shown to be useful for solving anomaly detection, the task of interest e.g. (Golan & El-Yaniv, 2018) .
Self-supervised classification-based methods have been proposed with the object of image anomaly detection, but we show that by generalizing the class of transformations they can apply to all data types.
In this paper, we introduce a novel technique, GOAD, for anomaly detection which unifies current state-of-the-art methods that use normal training data only and are based on classification.
Our method first transforms the data into M subspaces, and learns a feature space such that inter-class separation is larger than intra-class separation.
For the learned features, the distance from the cluster center is correlated with the likelihood of anomaly.
We use this criterion to determine if a new data point is normal or anomalous.
We also generalize the class of transformation functions to include affine transformation which allows our method to generalize to non-image data.
This is significant as tabular data is probably the most important for applications of anomaly detection.
Our method is evaluated on anomaly detection on image and tabular datasets (cyber security and medical) and is shown to significantly improve over the state-of-the-art.
Choosing the margin parameter s: GOAD is not particularly sensitive to the choice of margin parameter s, although choosing s that is too small might cause some instability.
We used a fixed value of s = 1 in our experiments, and recommend this value as a starting point.
Unsupervised training: Although most of our results are semi-supervised i.e. assume that no anomalies exist in the training set, we presented results showing that our method is more robust than strong baselines to a small percentage of anomalies in the training set.
We further presented results in other datasets showing that our method degrades gracefully with a small amount of contamination.
Our method might therefore be considered in the unsupervised settings.
Deep vs. shallow classifiers: Our experiments show that for large datasets deep networks are beneficial (particularly for the full KDDCUP99), but are not needed for smaller datasets.
For performance critical operations, our approach may be used in a linear setting.
This may also aid future theoretical analysis of our method.
In this paper, we presented a method for detecting anomalies for general unstructured data.
This was achieved by training a classifier on a set of random auxiliary tasks.
Our method does not require knowledge of the data domain, and we are able to generate an arbitrary number of random tasks.
We show that our method significantly improved over the state-of-the-art and even linear classifiers do so for most datasets.
Future work will extend this approach to generate arbitrary randomized tasks for time-series and image data.
We provide plots of the number of auxiliary tasks vs. the anomaly detection accuracy (measured by F 1 ) for all datasets.
The results are presented in Fig. 2 .
Performance increases rapidly up to a certain number of tasks (around 16).
For some datasets, the accuracy continues increasing at a diminishing rate.
Following this experiment, we improve our results by using more transformations for the smaller datasets (1024 up from the original 32 on Thyroid and 64 on Arrhythmia).
The results are presented in Tab.
5 as Affine-MaxM.
Using more tasks presents improvements on the smaller datasets.
An anomaly detection that:  uses random-transformation classification for generalizing to non-image data.