Attention Mechanisms
Dynamically prioritize certain parts of input data over others, enabling models to focus on relevant information when processing complex data sequences.
Attention mechanisms were introduced to improve the performance of neural networks by mimicking cognitive attention in human learning. They allow models to weigh the importance of different inputs differently, which is particularly useful in sequence-to-sequence tasks such as language translation, where the relevance of input elements can vary significantly. By focusing on the most relevant parts of the input data at each step of the computation, attention mechanisms can effectively handle long-range dependencies and improve the interpretability of model decisions. They are a cornerstone in state-of-the-art architectures, including Transformer models, which have revolutionized fields like Natural Language Processing (NLP) and Computer Vision (CV).
The concept of attention mechanisms in AI and neural networks began to gain prominence around 2014, with the introduction of the seq2seq model and its application to neural machine translation. The idea quickly evolved, and the Transformer model, introduced in 2017, fully utilized attention mechanisms, specifically self-attention, to achieve significant improvements in various tasks.
The introduction and development of attention mechanisms can be attributed to multiple researchers across several studies. Notably, Bahdanau et al. (2014) introduced one of the first models to successfully implement attention in neural machine translation. Vaswani et al. further revolutionized the field with the publication of "Attention Is All You Need" in 2017, introducing the Transformer model, which relies entirely on attention mechanisms without the use of recurrence or convolution layers.