Convolutional Deep Gaussian Processes

Singh, Vaibhav and Srijith, P K (2018) Convolutional Deep Gaussian Processes. Masters thesis, Indian Institute of Technology Hyderabad.

Thesis_Mtech_CS_4073.pdf - Submitted Version

Download (954kB) | Preview


Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alternative to standard parametric deep learning models. A DGP is formed by stacking multiple GPs resulting in a well-regularized composition of functions. The Bayesian framework that equips the model with attractive properties, such as implicit capacity control and predictive uncertainty, makes it at the same time challenging to combine with a convolutional structure. This has hindered the application of DGPs in computer vision tasks, an area where deep parametric models (i.e. CNNs) have made breakthroughs. Standard kernels used in DGPs such as radial basis functions (RBFs) are insufficient for handling pixel variability in raw images. In this paper, we build on the recent convolutional GP to develop Convolutional DGP (CDGP) models which effectively capture image level features through the use of convolution kernels, therefore opening up the way for applying DGPs to computer vision tasks. Our model learns local spatial influence and outperforms strong GP based baselines on multi-class image classification. We also consider various constructions of convolution kernel over the image patches, analyze the computational trade-offs and provide an efficient framework for convolutional DGP models. The experimental results on image data such as MNIST, rectangles-image, CIFAR10, Convex-sets and Caltech101 demonstrate the effectiveness of the proposed approaches. We also propose a method to reduce the computational complexity of the model. We sub-sample the number of patches and show the efficiency of the approach on caltech101 dataset.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Item Type: Thesis (Masters)
Uncontrolled Keywords: Gaussian Process, Bayesian Deep eearning, Convolutional Neural Network, Variational Infrance
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 26 Jun 2018 04:31
Last Modified: 31 Jul 2019 09:59
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 4073 Statistics for this ePrint Item