What is Transformer-Based Architecture? (Deep learning model)
2Transformer-based architectures are a groundbreaking class of neural network models that have revolutionized the field of artificial intelligence. They are designed to process sequential data, such as text or images, by leveraging a novel mechanism called "self-attention."
In a transformer, input data is divided into smaller segments, like words in a sentence or patches in an image. These segments are transformed into numerical vectors, and the model uses self-attention to weigh the importance of each segment in relation to the others. This allows the model to capture complex dependencies and relationships within the data, making it exceptionally effective for tasks involving context, like understanding language or recognizing patterns in images.
Transformers have achieved remarkable success in natural language processing tasks, powering chatbots, language translation, and sentiment analysis. They've also made their mark in computer vision with models like Vision Transformers (ViTs), which apply the same principles to images, achieving state-of-the-art results in image classification and other vision tasks.
These architectures have become a cornerstone of modern AI and continue to drive advancements in various fields, from healthcare to finance, making them a crucial topic for anyone interested in the future of artificial intelligence.