What is the principle of the attention mechanism?
Attention mechanism is a technique used in machine learning where the model can selectively focus on different parts of the input based on the requirements of the task. It works by calculating the correlation between different parts of the input and the current state of the model, assigning different weights based on the size of the correlation. This allows the model to selectively focus on parts relevant to the current task and ignore those that are not.
The attention mechanism typically consists of the following steps:
- Calculate correlation: Based on the input and the current state of the model, calculate the correlation between different parts of the input and the model state. Common methods include dot product, weighted dot product, inner product, etc.
- Calculating weights: Based on the calculation results of relevance, the weights of each input section are obtained through normalization of the relevance. This means that sections with higher relevance will receive higher weights, while sections with lower relevancy will receive lower weights.
- Weighted sum: calculate the final attention representation by summing up the input parts according to their weights. The attention representation will focus more on the parts relevant to the task while ignoring the irrelevant ones.
By using attention mechanisms, models can selectively focus on different parts of the input based on the requirements of the task, which can improve the performance and generalization capabilities of the model. Attention mechanisms are widely used in tasks such as natural language processing, computer vision, machine translation, text summarization, and image classification.
