Transformer Architecture: What Is a Transformer?

Transformers, a groundbreaking neural network architecture introduced in the 2017 paper "Attention Is All You Need," have significantly advanced fields such as natural language processing (NLP) and computer vision. By employing a self-attention mechanism, transformers can handle sequence-to-sequence tasks without the need for recurrence or convolutions, making them highly effective for text summarization, translation, and image classification. Vision Transformers (ViTs) leverage this architecture for visual tasks, achieving impressive results by processing images as sequences of fixed-size patches, unlike traditional convolutional neural networks (CNNs). While ViTs excel in capturing global image features and demonstrate competitive performance, especially on large datasets, they lack the inductive biases of CNNs and therefore may require substantial regularization and data augmentation. Self-supervised pre-training has propelled transformers' success in NLP, exemplified by models like GPT and BERT, but similar efficacy in computer vision remains under exploration. Despite these challenges, transformers continue to be a focus of research and development, promising further innovations in both NLP and computer vision applications.