Continual Learning Method for image classification in computer vision
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Date
2024
Authors
Kreshchenko, Taras
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Abstract
The paper explores the hypothesis that Continual Learning (CL) methods can improve the performance of a deep learning model in a traditional machine learning scenario. By augmenting an existing state-of-the-art ML solution to a problem with CL techniques, this research aims to demonstrate that AI can still achieve more accurate and adaptive performance. This hypothesis is tested on a parking lot occupancy detection problem, a binary classification problem that is well-suited to CL due to the continuous stream of image data. Experiments are conducted to compare the proposed CL-based solution and a contemporary solution that is non-CL based.
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Keywords
deep learning, continual learning, incremental learning, lifelong learning, domain adaptation, contrastive learning, CNN, computer vision, binary classification, parking, occupancy detection, мasters thesis