Attention Block
Core component in neural networks, particularly in transformers, designed to selectively focus on the most relevant parts of an input sequence when making predictions.
Attention blocks are critical for tasks involving sequences, such as language translation and image analysis, where understanding dependencies between distant elements is crucial. These blocks use a mechanism that assigns different importance (or "attention") to different parts of the input, enabling the model to weigh some parts of the sequence more heavily when producing its output. In a transformer architecture, attention blocks typically compute three key elements—query, key, and value—and use them to determine which positions in the input sequence should be attended to for each output. By enabling parallel computation and avoiding the need for sequential processing, attention blocks significantly improve efficiency and performance in tasks like natural language processing (NLP) and computer vision.
Brief Attention mechanisms were first introduced in 2014 in the context of neural machine translation but gained widespread popularity after the introduction of the transformer model in 2017 by Vaswani et al., which eliminated the need for recurrent networks by relying entirely on attention blocks.
The development of attention blocks was significantly advanced by the work of Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio in 2014, with further formalization by Ashish Vaswani, Noam Shazeer, and colleagues in their seminal 2017 paper, "Attention is All You Need."