Wasserstein generative adversarial active learning for anomaly detection with gradient penalty

Download

HasanAliDuran_Thesis.pdf

Date

2021-9

Author

Duran, Hasan Ali

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

545
views

422
downloads

Anomaly detection has become a very important topic with the advancing machine learning techniques and is used in many different application areas. In this study, we approach differently than the anomaly detection methods performed on standard generative models and describe anomaly detection as a binary classification problem. However, in order to train a highly accurate classifier model, the number of anomaly data in data-sets is very limited, and with synthetic data produced using generative models, it can be brought to a usable level to train the model. In our model like GANs while Generator produces potential informative anomaly data, the Discriminator tries to determine whether the generated data is fake or real. In addition to these, we have added the Critic network to our model in order to enable the Generator to produce more realistic and informative data. In this way, we designed our model the Discriminator to be trained with the data produced by the Generator which is improved by the Critic network. Therefore, after sufficient training, the Discriminator turns into a natural anomaly detection classification tool. Since the Generator produce more realistic data in each epoch during the training phase, created ones more informative potential anomaly data for the Discriminator, which will allow the algorithm to develop with more informative data with active learning logic. Our novelty is a generative adversarial active learning (GAAL) structure designed over the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) instead of just applying this method over the standard GAN model. Both our Generator model can produce more realistic and more informative data than before, and at the same time, it prevents the mode collapse problem, which is one of the biggest problems of the standard GAN model. We have obtained a model that can detect anomalies with higher accuracy. Improved version of Wasserstein Generative Adversarial Active Learning (WGAAL-GP) has been tested on different data sets and the results obtained are presented by comparing them with previous studies.

Subject Keywords

Deep Learning, Machine Learning, Generative Models, GAN, Wasserstein Gan, Outlier Detection, Anomaly Detection, Active Learning

URI

https://hdl.handle.net/11511/93171

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Deep learning approach for laboratory mice grimace scaling Eral, Mustafa; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2016) Deep learning is extremely attractive research topic in pattern recognition and machine learning areas. Applications in speech recognition, natural language processing, and machine vision fields gained huge acceleration in performance by employing deep learning. In this thesis, deep learning is used for medical purposes in order to scale pain degree of drug stimulated mice by examining facial grimace. For this purpose each frame in the videos in the training set were scaled manually by experts according to ...
A deep learning approach for the transonic flow field predictions around airfoils Duru, Cihat; Alemdar, Hande; Baran, Özgür Uğraş (2022-01-01) Learning from data offers new opportunities for developing computational methods in research fields, such as fluid dynamics, which constantly accumulate a large amount of data. This study presents a deep learning approach for the transonic flow field predictions around airfoils. The physics of transonic flow is integrated into the neural network model by utilizing Reynolds-averaged Navier–Stokes (RANS) simulations. A detailed investigation on the performance of the model is made both qualitatively and quant...
Deep Learning-Based Hybrid Approach for Phase Retrieval IŞIL, ÇAĞATAY; Öktem, Sevinç Figen; KOÇ, AYKUT (2019-06-24) We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.
On numerical optimization theory of infinite kernel learning Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2010-10-01) In Machine Learning algorithms, one of the crucial issues is the representation of the data. As the given data source become heterogeneous and the data are large-scale, multiple kernel methods help to classify "nonlinear data". Nevertheless, the finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, a novel method of "infinite" kernel combinations is proposed with the help of infinite and semi-infinite programming regarding all elements in kernel space. Look...
Competing labels: a heuristic approach to pseudo-labeling in deep semi-supervised learning Bayrak, Hamdi Burak; Ertekin Bolelli, Şeyda; Yücel, Hamdullah; Department of Scientific Computing (2022-2-10) Semi-supervised learning is one of the dominantly utilized approaches to reduce the reliance of deep learning models on large-scale labeled data. One mostly used method of this approach is pseudo-labeling. However, pseudo-labeling, especially its originally proposed form tends to remarkably suffer from noisy training when the assigned labels are false. In order to mitigate this problem, in our work, we investigate the gradient sent to the neural network and propose a heuristic method, called competing label...

Citation Formats

H. A. Duran, “Wasserstein generative adversarial active learning for anomaly detection with gradient penalty,” M.S. - Master of Science, Middle East Technical University, 2021.