The Rise of Transformer Models beyond NLP

Transformers have changed the way we handle sequential data, but their impact extends far beyond language models. Read on to discover how they’re revolutionizing computer vision and audio processing.

Background on Transformers

Long before GPT-3, the transformer architecture was proposed in “Attention is All You Need.” It replaced RNNs and LSTMs with a simpler, more parallelizable mechanism.

Beyond Text: Computer Vision

Vision Transformers (ViT) apply the same self-attention mechanisms to patches of images. This allows for global context capture that CNNs often struggle with.

Audio Processing and Beyond

From speech-to-text to music generation, transformers are proving to be the universal architecture for sequential data.