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International Research Journal of Engineering Science, Technology and Innovation

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Rajendra Nath

Department of Computer Science, India


  • Review Article   
    Enhancing Machine Learning Performance through Transfer Learning and Data Augmentation Techniques
    Author(s): Rajendra Nath*

    Machine learning algorithms have gained significant attention in recent years due to their ability to extract meaningful patterns and insights from vast amounts of data. However, achieving optimal performance often requires a substantial amount of labeled data, which can be expensive and time-consuming to acquire. In this research article, we propose a novel approach to enhance machine learning performance by combining transfer learning and data augmentation techniques. Transfer learning leverages pre-trained models on large datasets to bootstrap the learning process on smaller, domain-specific datasets. By utilizing the knowledge learned from the source task, transfer learning can improve generalization and speed up convergence on the target task. Data augmentation, on the other hand, increases the size and diversity of the training dataset by applying various transformations such as.. View More»
    DOI: 10.14303/2315-5663.2023.107

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