Vol. 4 No. 1 (2024): Advances in Deep Learning Techniques
Articles

Transformer Networks - Architectures and Applications: Investigating Transformer Network Architectures and Their Diverse Applications in Natural Language Processing and Beyond

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  • Prof. Kimiko Tanaka
Prof. Kimiko Tanaka
Professor of Computer Vision, University of Tokyo, Japan
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Published 27-02-2024

Keywords

  • Transformer Networks,
  • Attention Mechanism,
  • Natural Language Processing,
  • Deep Learning,
  • Machine Translation,
  • Text Generation,
  • Computer Vision,
  • Speech Recognition,
  • Applications,
  • Challenges
    • ...More
    Less

How to Cite

[1]
P. K. Tanaka, “Transformer Networks - Architectures and Applications: Investigating Transformer Network Architectures and Their Diverse Applications in Natural Language Processing and Beyond”, Adv. in Deep Learning Techniques, vol. 4, no. 1, pp. 1–17, Feb. 2024.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Transformer Networks, since their introduction in the seminal paper "Attention is All You Need," have revolutionized the field of natural language processing (NLP) and found wide-ranging applications beyond NLP. This paper provides a comprehensive overview of transformer network architectures and their diverse applications. We start by explaining the core components of transformer networks, including self-attention mechanisms and feed-forward neural networks. We then delve into various transformer-based architectures, such as BERT, GPT, and T5, highlighting their unique features and improvements over the original transformer model.

Furthermore, we explore the applications of transformer networks in NLP tasks, such as machine translation, text summarization, and question answering. We also discuss their use in computer vision, speech recognition, and other domains. Additionally, we examine the challenges and limitations of transformer networks, including computational complexity and fine-tuning requirements.

Overall, this paper aims to provide a comprehensive understanding of transformer networks, their architectures, and their wide-ranging applications, showcasing their significance in advancing the field of deep learning and artificial intelligence.

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