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Tesi etd-04112020-004620


Thesis type
Tesi di laurea magistrale
Author
BROMBIN, LUCA
URN
etd-04112020-004620
Title
Developing and Experimenting Deep Learning Methods for Unsupervised Anomaly Detection in Images
Struttura
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
COMPUTER ENGINEERING
Supervisors
relatore Prof. Falchi, Fabrizio
Parole chiave
  • deep learning
  • neural network
  • machine learning
  • anomaly detection
  • industry 4.0
Data inizio appello
05/05/2020;
Consultabilità
Secretata d'ufficio
Riassunto analitico
Anomaly detection in the industrial sector is an important problem as it is a key component of
quality control systems that minimize the chance to miss a defective product. Most often, the
anomaly detection is done through analysis of the images of the products. Because the products,
or their designs change and quality data is hard to obtain, this problem is approached in an unsupervised manner. There are many different anomaly detection approaches, but most of them deal with low dimensional data and do not work well with the images. We examine deep learning techniques that utilize convolutional neural networks which can extract meaningful image representations to a lower-dimensional space. It allows the models to learn the important features of an image, regardless of some small changes in the input. The feature extracted with the CNN are used to train standard one class classifier (such as one class support vector machine) that is able to classify an object in an unsupervised way.
Then next approach is an anomaly detection based on generative adversarial networks (GAN). The network learns a mapping from the latent space to a representation of a “normal” data and is able to produce new and unseen data samples from random latent vectors. In particular, the main state of the art models for anomaly detection based on GAN were examined. Afterwards has been developed a new model (based on BiGAN) to detect anomalies to remove the problems of the standard methods and increase the performance called CBiGAN.
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