convolutional neural network thesis pdf

Designing a Convolutional Neural Network for Image Recognition: A Comparative Study of Different Architectures and Training Techniques

28 Pages Posted: 27 Feb 2023

Tapomoy Adhikari

Microsoft Corporation

Date Written: February 22, 2023

A powerful tool for image recognition, Convolutional Neural Networks (CNNs) have been successfully applied in various fields including computer vision, medical image analysis, and self- driving cars. However, when dealing with large datasets, selecting the right architecture and training technique for a CNN can be challenging. This thesis aims to identify the most effective approach for image recognition by comparing different CNN architectures and training techniques. The literature review provides an overview of CNNs for image recognition, discussing various architectures and training techniques that have been used in previous studies. The review explains common CNN architectures such as LeNet, AlexNet, VGG, and ResNet, highlighting their strengths and weaknesses. The literature also covers popular training techniques, including SGD, Adam, and BN. The study used the CIFAR-10 dataset, comprising 60,000 color images of 32x32 pixels, classified into ten different classes. The data was preprocessed by normalizing pixel values and augmenting the training set with random flips and rotations. The researchers implemented and trained seven different CNN architectures, including LeNet, AlexNet, VGG-16, VGG-19, ResNet-50, ResNet-101, and ResNet-152, using three different training techniques: SGD, Adam, and SGD with BN. The results show that ResNet-152 was the most effective architecture for the CIFAR-10 dataset, achieving an accuracy of 94.7%. ResNet-101 and VGG-19 followed closely, both achieving an accuracy of 93.7%. Deeper networks performed better than shallower ones, with ResNet-152, which has 152 layers, outperforming VGG-19, which has 19 layers. Adding BN to the SGD training technique improved the performance of the CNN architectures, resulting in higher accuracy and faster convergence. This comparative study provides valuable insights into the performance of different CNN architectures and training techniques for image recognition. The findings demonstrate the importance of selecting the appropriate CNN architecture and training technique for achieving high accuracy in image recognition tasks. The study highlights the importance of the training technique, with the addition of BN to SGD resulting in improved performance. The implications of these findings are practical, as they could assist researchers and practitioners in the field of image recognition in designing CNNs. Future research could investigate the performance of these CNN architectures and training techniques on other datasets and explore other state-of-the-art techniques, such as transfer learning and adversarial training. This study's findings have potential for wider application in various fields where CNNs are used for image recognition, including self-driving cars, medical image analysis, and computer vision.

Keywords: Convolutional Neural Network, Machine Learning

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Master Thesis on Bayesian Convolutional Neural Network using Variational Inference

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Master thesis: bayesian convolutional neural networks.

Thesis work submitted at Computer Science department at University of Kaiserslautern.

• Kumar Shridhar

Supervisors

• Prof. Marcus Liwicki (Professor at Luleå University, Sweden)
• Felix Laumann (PhD candidate at Imperial College, London)

Artificial Neural Networks are connectionist systems that perform a given task by learning on examples without having a prior knowledge about the task. This is done by finding an optimal point estimate for the weights in every node. Generally, the network using point estimates as weights perform well with large datasets, but they fail to express uncertainty in regions with little or no data, leading to overconfident decisions.

In this thesis, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights. Furthermore, the proposed BayesCNN architecture is applied to tasks like Image Classification, Image Super-Resolution and Generative Adversarial Networks.

BayesCNN is based on Bayes by Backprop which derives a variational approximation to the true posterior. Our proposed method not only achieves performances equivalent to frequentist inference in identical architectures but also incorporate a measurement for uncertainties and regularisation. It further eliminates the use of dropout in the model. Moreover, we predict how certain the model prediction is based on the epistemic and aleatoric uncertainties and finally, we propose ways to prune the Bayesian architecture and to make it more computational and time effective.

In the first part of the thesis, the Bayesian Neural Network is explained and it is applied to an Image Classification task. The results are compared to point-estimates based architectures on MNIST, CIFAR-10, CIFAR-100 and STL-10 datasets. Moreover, uncertainties are calculated and the architecture is pruned and a comparison between the results is drawn.

In the second part of the thesis, the concept is further applied to other computer vision tasks namely, Image Super-Resolution and Generative Adversarial Networks. The concept of BayesCNN is tested and compared against other concepts in a similar domain.

The proposed work has been implemented in PyTorch and is available here : BayesianCNN

Chapter Overview

Chapter 1 : introduction.

Why there is a need for Bayesian Networks?

Problem Statement

Current Situation

Our Hypothesis

Our Contribution

Chapter 2: Background

Neural Networks and Convolutional Neural Networks

Concepts overview of Variational Inference, and local reparameterization trick in Bayesian Neural Network.

Backpropagation in Bayesian Networks using Bayes by Backprop.

Estimation of Uncertainties in a network.

Pruning a network to reduce the number of overall parameters without affecting it's performance.

Chapter 3: Related Work

How Bayesian Methods were applied to Neural Networks for the intractable true posterior distribution.

Various ways of training Neural Networks posterior probability distributions: Laplace approximations, Monte Carlo and Variational Inference.

Proposals on Dropout and Gaussian Dropout as Variational Inference schemes.

Work done in the past for uncertainty estimation in Neural Network.

Ways to reduce the number of parameters in a model.

Chapter 4: Concept

Bayesian CNN with Variational Inference based on Bayes by Backprop.

Bayesian convolutional operations with mean and variance.

Local reparameterization trick for Bayesian CNN.

Uncertainty estimation in a Bayesian network.

Using L1 norm for reducing the number of parameters in a Bayesian network.

Chapter 5: Empirical Analysis

Applying Bayesian CNN for the task of Image Recognition on MNIST, CIFAR-10, CIFAR-100 and STL-10 datasets.

Comparison of results of Bayesian CNN with Normal CNN architectures on similar datasets.

Regularization effect of Bayesian Network with dropouts.

Distribution of mean and variance in Bayesian CNN over time.

Parameters comparison before and after model pruning.

Chapter 6: Applications

Empirical analysis of BayesCNN with normal architecture for Image Super Resolution.

Empirical analysis of BayesCNN with normal architecture for Generative Adversarial Networks.

Chapter 7: Conclusion and Outlook

• Experiment Specification
• How to replicate results

Journal paper of this work is also available on Arxiv: A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

Feel free to cite, if the work is of any help to you:

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Convolutional neural network-based soil water content and density prediction model for agricultural land using soil surface images, 1. introduction, 2. materials and methods, 2.1. soil surface image acquisition, 2.2. data preparation for the cnn model, 2.3. convolutional neural network, 3.1. quality of original and segmented images, 3.2. output of cnn model, 3.3. accuuracy of soil water content and soil dry density prediction, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Kim, D.; Kim, T.; Jeon, J.; Son, Y. Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images. Appl. Sci. 2023 , 13 , 2936. https://doi.org/10.3390/app13052936

Kim D, Kim T, Jeon J, Son Y. Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images. Applied Sciences . 2023; 13(5):2936. https://doi.org/10.3390/app13052936

Kim, Donggeun, Taejin Kim, Jihun Jeon, and Younghwan Son. 2023. "Convolutional Neural Network-Based Soil Water Content and Density Prediction Model for Agricultural Land Using Soil Surface Images" Applied Sciences 13, no. 5: 2936. https://doi.org/10.3390/app13052936

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