Thesis

Visual feature extraction through brain inspired algorithms : towards efficiency, accuracy and continual learning

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Awarding institution
  • University of Strathclyde
Date of award
  • 2025
Thesis identifier
  • T17248
Person Identifier (Local)
  • 202067948
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Abstract
  • Machine learning and Artificial intelligence have already revolutionised the world we live in. Nevertheless, these technologies are expected to progress even further and advance living standards far beyond today’s reality. To achieve this revolution, several limitations of current AI need to be addressed. Demands on computing resources and energy supply are a major obstacle, which ultimately limit the capabilities of the AI we can deploy at the edge. Furthermore, current systems are ill-suited for continual learning (CL) on real world data, as they require to train with all available data as an independent and identically distributed (i.i.d.) set. This thesis focuses on these problems by working on visual feature extraction, and contributing to more efficient and accurate algorithms, with the capacity for CL. With the objective of developing energy efficient feature extraction, a major part of the thesis is focused on Spiking Neural Networks (SNNs). SNNs have become an interesting alternative to conventional artificial neural networks (ANN) thanks to their temporal processing capabilities and energy efficient implementations in neuromorphic hardware. However, the challenges involved in training them have limited their performance in terms of accuracy and thus their applications. Improving learning algorithms and neural architectures for a more accurate feature extraction is therefore a priority. Contributing towards this aim, this work presents a study on the key components of modern spiking architectures, an indepth study on the possible implementations of spiking residual connections, and a novel spiking version of the successful residual network architecture. Additionally, the effect of different state of the art techniques are empirically compared in image classification tasks to provide a state of the art guide to SNN design. Finally, the proposed network outperforms previous SNN architectures in multiple datasets, while using less parameters. In order to exploit SNNs for more efficient AI, it is also of interest to understand the full scope of their exploitable properties. These networks are characterised by their unique temporal dynamics, but the properties and advantages of such computations are still not fully understood. In order to provide answers, in this work it is demonstrated how spiking neurons can enable temporal feature extraction in feed-forward neural networks without the need for recurrent synapses, and how recurrent SNNs can achieve comparable results to LSTM with a smaller number of parameters. This shows how their bio-inspired computing principles can be successfully exploited beyond energy efficiency gains, and evidences their differences with respect to conventional artificial neural networks. These results are obtained through a new task, DVS-Gesture-Chain (DVS-GC), which allows, for the first time, to evaluate the perception of temporal dependencies in a real event-based action recognition dataset. Furthermore, this setup allows to reveal the role of the leakage rate in spiking neurons for temporal processing tasks and demonstrated the benefits of ”hard reset” mechanisms. Finally, the focus is switched to the capacity for training feature extractors in continual learning scenarios, a major milestone for the development of truly autonomous systems and artificial general intelligence. The challenge in this setup is to avoid catastrophic forgetting, where artificial systems forget previous knowledge if they are trained in new data without revisiting the old. Often, the methods used to alleviate forgetting make use of either rehearsal buffers, pretrained backbones or external indication of the task to solve. However, these requirements result in severe limitations regarding scalability, privacy preservation and efficient deployment. This work explores how to eliminate the need for such requirements and proposes a new method, Low Interference Feature Extraction Sub-networks (LIFES). Additionally, the study breaks down the Catastrophic Forgetting (CF) problem into 4 causes, allowing to better understand the effect of CL methods. The proposed LIFES algorithm achieves competitive results in standard incremental learning scenarios, providing an alternative to approaches with more restrictive requirements. Moreover, it provides solutions for specific causes of the CF problem, making it complementary to other methods
Advisor / supervisor
  • Di Caterina, Gaetano
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