The document discusses deep semi-supervised anomaly detection (DeepSAD) which addresses limitations of shallow supervised and deep unsupervised techniques for identifying outliers. It presents the framework's architecture, performance metrics, and experimental comparisons using various datasets, showing that DeepSAD outperforms existing methods in scenarios with labeled anomalous examples. The conclusion emphasizes the generalizability of DeepSAD and suggests future research directions in anomaly detection.

1. Deep Semi-Supervised Anomaly
Detection (DeepSAD)
By Manmeet Singh
Original paper by Ruff, et al. [1]

2. Introduction
● Anomaly detection is the task of identifying outliers in the given data
● Shallow supervised techniques:
○ Require manual feature engineering
○ Are less effective on high-dimensional data
○ Are limited in scalability to large datasets
● Deep unsupervised techniques only utilize labeled normal data. Existing methods are:
○ Domain specific
○ Heavily biased towards classification tasks
● In a real world use case, one may have some labeled anomalous examples, in addition
to the normal data
○ Anomalous data can belong to various distributions

3. Existing
Techniques
● Training data consists of:
○ labeled
○ unlabeled data
○ labeled anomalies
● Contour maps represents
gradient of what each algorithm
learned as the representation
of normal data
● Semi-supervised anomaly
detection defines a much more
crisp boundary around normal
data distributions

4. Information Theory Context
Supervised deep learning minimizes mutual information between input (X) and
latent representation (Z), while maximizing it between latent representation and the
classification task (Y).
The objective of unsupervised learning, based on the infomax principle, is to
maximize mutual information between the input (X) and its latent representation (Z)
Choices for regularization (R) include sparsity or distance to prior distribution (KL divergence), or dimensionality
constraints.
(1)
(2)

5. Unsupervised
Deep SVDD
Precursor to Deep SAD
● Includes label information Y
through regularization objective
R(Z) = R(Z; Y ) that is based on
entropy
● Using MSE forces the network
to extract those common
factors of variation which are
most stable within the dataset
● In probabilistic terms, this is
entropy minimization over
latent distribution
(3)

6. DeepSAD ● η (eta) is hyperparameter controlling the amount
of emphasis placed on labeled vs unlabeled
data
● Parameter m represents labeled samples, in
addition to n unlabeled samples seen in Deep
SVDD
● Unlabeled loss and regualizer expressions are
same as Deep SVDD.
○ New addition is the middle term
● Deep SAD overall follows the same process as
Deep SVDD, but by replicating the expression
used for unlabeled data and modifying it for
labeled data
● This method does not impose any cluster
assumption on the anomaly-generating
distribution
○ Normal and anomalous distributions are
learned

7. Benchmarks
● Datasets: MNIST, Fashion-MNIST,
CIFAR-10
● Test setup: For multi-class datasets
use one class as “normal” and rest
as anomalous
● 3 scenarios for performance
comparison
1. Ratio of labeled anomalies to
unlabeled anomalies
Results: Deep SAD (pink color)
generalizes better as more labeled
anomalies are presented for
training

8. Benchmarks cont.
2. Ratio of pollution (outlier types) in
the unlabeled training data
Results: Performance of all methods
decreases with increasing data pollution.
Deep SAD proves to be most robust
3. Number of anomaly classes
included in the training data
Results: As the number of anomaly
classes increases, Deep SAD performs
better

9. Inter-class sensitivity analysis
● The hyperparameter eta was varied
from {10^-2, … , 10^2} to analyze
sensitivity of Deep SAD with respect
to hyperparameter eta.
● Eta tunes the weight of labeled vs.
unlabeled training data distribution for
the model (see Eqn. 4).
○ Setting > 1 puts more emphasis on the labeled data
whereas < 1 emphasizes the unlabeled data
● Overall, the loss function is fairly
robust to increments in labeled data

10. Conclusion and Future Work
● Supervised methods still perform better with small datasets but Deep SAD is competitive
○ Better in large datasets with multiple anomalous distributions
● Deep SAD is not domain or problem specific
○ It is a generalization of unsupervised Deep SVDD method
● General semi-supervised anomaly detection should be preferred whenever some
labeled information on both normal and anomalies samples is available
● Potential future works can include more rigorous analysis and studying deep anomaly
detection under rate distortion curve, for example.

11. Thank you

12. References
[1] Lukas Ruff, et al. "Deep Semi-Supervised Anomaly Detection." International
Conference on Learning Representations.
[2] Lukas Ruff, Robert A Vandermeulen, Nico Görnitz, Lucas Deecke, Shoaib A Siddiqui,
Alexander Binder, Emmanuel Müller, and Marius Kloft. Deep one-class classification. In
ICML, volume 80, pp. 4390–4399, 2018.