Attention networks, especially prominent in transformer architectures, allow models to allocate varying levels of focus to different parts of the input data. This mechanism enhances the network's ability to handle long-range dependencies and contextual information effectively. By calculating attention scores, the model determines which parts of the input are more relevant for generating the output, improving both efficiency and accuracy in tasks like machine translation, summarization, and image captioning. Self-attention, a form of attention mechanism, computes these scores by relating different positions of a single sequence to capture internal dependencies, significantly boosting performance in NLP applications.
Historical Overview: The concept of attention in neural networks first emerged in the early 2010s, gaining substantial popularity with the introduction of the attention mechanism in sequence-to-sequence models by Bahdanau et al. in 2014. It revolutionized the field with the advent of the Transformer model by Vaswani et al. in 2017, which relied solely on self-attention mechanisms.
Key Contributors: Key contributors to the development of attention networks include Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, who introduced the attention mechanism in neural machine translation in 2014. Ashish Vaswani and his colleagues further advanced the concept with the Transformer model in 2017, which has since become the foundation for state-of-the-art models like BERT and GPT.