Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample
  Generation on the Boundary   Generative models trained in an unsupervised manner may set high likelihood
and low reconstruction loss to Out-of-Distribution (OoD) samples. This
increases Type II errors and leads to missed anomalies, overall decreasing
Anomaly Detection (AD) performance. In addition, AD models underperform due to
the rarity of anomalies. To address these limitations, we propose the OoD
Minimum Anomaly Score GAN (OMASGAN). OMASGAN generates, in a negative data
augmentation manner, anomalous samples on the estimated distribution boundary.
These samples are then used to refine an AD model, leading to more accurate
estimation of the underlying data distribution including multimodal supports
with disconnected modes. OMASGAN performs retraining by including the abnormal
minimum-anomaly-score OoD samples generated on the distribution boundary in a
self-supervised learning manner. For inference, for AD, we devise a
discriminator which is trained with negative and positive samples either
generated (negative or positive) or real (only positive). OMASGAN addresses the
rarity of anomalies by generating strong and adversarial OoD samples on the
distribution boundary using only normal class data, effectively addressing mode
collapse. A key characteristic of our model is that it uses any f-divergence
distribution metric in its variational representation, not requiring
invertibility. OMASGAN does not use feature engineering and makes no
assumptions about the data distribution. The evaluation of OMASGAN on image
data using the leave-one-out methodology shows that it achieves an improvement
of at least 0.24 and 0.07 points in AUROC on average on the MNIST and CIFAR-10
datasets, respectively, over other benchmark and state-of-the-art models for
AD.

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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: plasm-ph