Feature Dimensionality Reduction with Variational Autoencoders in Deep Bayesian Active Learning

Date

2021-06-09

Author

Ertekin Bolelli, Şeyda

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Data annotation for training of supervised learning algorithms has been a very costly procedure. The aim of deep active learning methodologies is to acquire the highest performance in supervised deep learning models by annotating as few data points as possible. As the feature space of data grows, the application of linear models in active learning settings has become insufficient. Therefore, Deep Bayesian Active Learning methodology which represents model uncertainty has been widely studied. In this paper, a study has been conducted in order to increase the performance of Deep Bayesian Active Learning method. Feature dimensionality reduction is performed on data set by using Variational Autoencoder model. Low dimensional data is used to train a Bayesian Multi Layer Perceptron. The proposed method outperformed the Bayesian Multi Layer Perceptron model which is trained on entire feature space in terms of accuracy performance. The accuracy of the proposed method is tested on baseline datasets.

Subject Keywords

Deep Active Learning, Variational Autoencoder, Bayesian Neural Networks, Feature Learning

URI

https://hdl.handle.net/11511/91417

DOI

https://doi.org/10.1109/SIU53274.2021.9477979

Conference Name

29th Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Ş. Ertekin Bolelli, “Feature Dimensionality Reduction with Variational Autoencoders in Deep Bayesian Active Learning,” İstanbul, Türkiye, 2021, vol. 1, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/91417.