This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
Search
Skip to Search Results- 5Image Classification
- 3Deep Learning
- 2Computer Vision
- 2Machine Learning
- 1Artificial Intelligence
- 1Bark Beetle Attack Stage Classification
-
Spring 2022
Data augmentation is a strong tool for enhancing the performance of deep learning models using different techniques to increase both the quantity and diversity of training data. Cutout was previously proposed, in the context of image classification, as a simple regularization technique that...
-
Advancing Forest Health Monitoring: Harnessing the Power of Deep Learning Computer Vision for Remote Sensing Applications
DownloadFall 2023
Forests provide immense economic, ecological, and societal values, making forest health monitoring (FHM) a crucial task for guiding conservation and management of these essential ecosystems. Drones have seen increased popularity in this domain due to their ability to collect high-resolution,...
-
Fall 2021
Malware classification is a critical task in cybersecurity. It offers insights on the threats posed to victim devices from different malware and aids in the designing of precautionary measures. In real world applications, due to the vast amount of malware present in the networks, real-time...
-
Fall 2024
The success of deep learning is partly due to the sheer size of modern models. However, such large models strain the capabilities of mobile or resourceconstrained devices. Ergo, reducing the resource demands of AI models is essential before AI can be deployed on such devices. One promising...
-
Spring 2016
Image classification is an important problem in machine learning. Deep neural networks, particularly deep convolutional networks, have recently contributed great improvements to end-to-end learning quality for this problem. Such networks significantly reduce the need for human designed features...