What Attention Mechanism Meaning, Applications & Example
Neural network component that helps models focus on relevant parts of input data.
What is Attention Mechanism?
An Attention Mechanism is a technique in neural networks that allows models to focus on the most relevant parts of the input data when making predictions. This mechanism dynamically weighs input elements, enabling the model to prioritize specific information, which is especially useful in handling long sequences like text or speech.
Types of Attention Mechanism
- Self-Attention: Computes attention within a sequence, such as in transformer models, to identify relationships between elements.
- Soft and Hard Attention: Soft attention assigns weights to all elements, while hard attention focuses on specific parts of the input, often with stochastic sampling.
- Multi-Head Attention: Combines multiple attention heads to capture varied information from different input segments, improving model depth.
Applications of Attention Mechanism
- Machine Translation: Enhances translation accuracy by aligning words in the source and target languages.
- Text Summarization: Helps focus on key points when condensing documents.
- Image Captioning: Directs the model’s focus on relevant image parts to generate accurate descriptions.
Example of Attention Mechanism
In transformer-based models like GPT, the attention mechanism enables the model to assign higher importance to specific words in a sentence, helping it generate more contextually accurate responses.