Model optimisation techniques for convolutional neural networks

Sajid Nazir, Shushma Patel, Dilip Patel

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review


Deep neural networks provide good results for computer vision tasks. This has been possible due to a renewed interest in neural networks, availability of large-scale labelled training data, virtually unlimited processing and storage on cloud platforms and high-performance clusters. A Convolutional Neural Network (CNN) is one such architecture better suited for image classification. An important factor for a better CNN performance, besides the data quality, is the choice of hyperparameters, which define the model itself. The model or hyperparameter optimisation involves selecting the best configuration of hyperparameters, but is challenging because the set of hyperparameters are different for each type of machine learning algorithm. Thus, it requires a lot of computational time and resources to determine a better performing machine learning model. Therefore, the process has a lot of research interest and currently a transition to a fully automated process is also underway. This paper provides a survey of the CNN model optimisation techniques proposed in the literature.
Original languageEnglish
Title of host publicationHandbook of Research on New Investigations in Artificial Life, AI, and Machine Learning
PublisherIGI Global
ISBN (Electronic)9781799886877
ISBN (Print)9781799886860
Publication statusPublished - Feb 2022


  • automated machine learning
  • deep learning
  • explainable artificial intelligence
  • model complexity
  • object recognition
  • convolution
  • classification
  • cloud computing
  • Neural Network Performance


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