Attention Mechanism
Technique allowing models to focus on relevant parts of input when making predictions, dramatically improving performance on sequential tasks.
Key Concepts
Query, Key, Value
The three components of the attention mechanism.
Attention Weights
The weights that are assigned to the values.
Context Vector
The output of the attention mechanism.
Detailed Explanation
The attention mechanism is a technique that allows a neural network to focus on relevant parts of an input sequence when making predictions. It was originally developed for machine translation, but it has since been applied to a wide variety of other tasks, including image captioning and text summarization.
The attention mechanism works by assigning a weight to each element in the input sequence. The weights are then used to compute a context vector, which is a weighted average of the input elements. The context vector is then used to make a prediction.
Real-World Examples & Use Cases
Machine Translation
The attention mechanism is used to align the words in the source and target sentences.
Image Captioning
The attention mechanism is used to focus on the most important parts of an image when generating a caption.
Text Summarization
The attention mechanism is used to identify the most important sentences in a document when generating a summary.