Attention

Attention is a mechanism in neural networks that allows the model to focus on specific parts of the input sequence when making predictions.

Attention is commonly used in natural language processing (NLP) and computer vision to improve the performance of models by allowing them to selectively focus on relevant parts of the input data.

Attention works by assigning a weight to each part of the input sequence, indicating its importance for the predicted output sequence. These weights are then used to create a weighted sum of the input features, which is used as the input for the next layer of the model. This allows the model to focus on the most relevant parts of the input sequence and ignore irrelevant information.

Attention mechanisms are a key component of transformer models, which have achieved state-of-the-art performance in many NLP tasks. Transformers use self-attention to process the entire input sequence in parallel, allowing them to capture complex dependencies and relationships between words.

Self-attention, a specific type of attention, allows the model to focus on different parts of the same input sequence. While attention is concerned with the relationship of two sequences (typically input and output), self-attention models the relationships within the same sequence.

This is particularly useful in tasks like language modeling, where the model needs to capture long-range dependencies between words in a sentence, e.g. what a certain pronoun refers to.

Attention is an essential technique in neural networks to build models that can selectively focus on relevant parts of the input data, improving their performance and interpretability.