Transformers in Machine Learning | 17 May 2023

Why in News?

In recent times, Machine Learning (ML) is experiencing a transformative shift with the rise of transformer models.

• Transformers have gained significant attention due to their ability to revolutionize language processing, image understanding, and more.
• The impact of transformers on diverse domains and their potential for positive outcomes have made them a hot topic in the news.

What are Transformers in ML?

• About:
  • Transformers are a type of deep learning model used for natural language processing (NLP) and computer vision (CV) tasks.
  • They utilize a mechanism called “self-attention” to process sequential input data.
  • Transformers can process the entire input data at once, capturing context and relevance.
  • They can handle longer sequences efficiently and overcome the vanishing gradients problem faced by recurrent neural networks (RNNs).
  • Transformers were introduced in 2017 through the paper "Attention is All You Need" by Google Brain.
  • They have become popular and led to the development of pre-trained system Generative Pre-trained Transformer(GPT).
• Understanding Transformers:
  • Transformers consist of an encoder and a decoder, which work together to process input and generate output.
    • The encoder converts words into abstract numerical representations and stores them in a memory bank.
    • The decoder generates words one by one, referring to the generated output and consulting the memory bank through attention.
• Function:
  • Self-Attention Mechanism in Transformers:
    • Attention in ML allows models to selectively focus on specific parts of the input when generating outputs.
    • It enables transformers to capture context and build relationships between different elements in the data.
  • Transformer Applications in Language Processing:
    • Transformers have revolutionized tasks such as language translation, sentiment analysis, text summarization, and natural language understanding.
    • They process entire sentences or paragraphs, capturing intricate linguistic patterns and semantic meaning.
  • Transformer Applications in Image Understanding:
    • Transformers have made significant strides in computer vision tasks, surpassing traditional convolutional neural networks (CNNs).
    • They analyze images by breaking them into patches and learning spatial relationships, leading to improved image classification, object detection, and more.
  • Versatility and Cross-Modal Applications:
    • Transformer’s ability to process multiple modalities, such as language and vision, has paved the way for joint vision-and-language models.
    • These models enable tasks like image search, image captioning, and answering questions about visual content.
• Evolution:
  • Evolution from Hand-Crafted Features to Transformers:
    • Traditional machine learning approaches relied on manually engineered features, specific to narrow problems.
    • Transformers, on the other hand, eliminate the need for hand-crafted features and learn directly from raw data.
  • Transformers in Computer Vision:
    • Transformers have found success in computer vision by dividing images into patches, resembling words in a sentence.
    • Trained on large datasets, transformers outperform traditional convolutional neural networks (CNNs) in image classification, object detection, and more.
• Recent Developments:
  • Large-Scale Transformer Models:
    • Recent advancements have seen the development of transformer models with billions or trillions of parameters.
      • These models, known as large language models (LLMs) like ChatGPT, exhibit impressive capabilities in tasks like question answering, text generation, and image synthesis.
• Challenges and Considerations:
  • Evaluating the performance and limitations of large-scale transformer models remains an ongoing challenge for researchers.
  • Concerns related to ethical use, privacy, and potential biases associated with these models need to be addressed.

Source: TH