Attention mechanism is a key concept in machine learning, particularly in the field of natural language processing (NLP) and computer vision. It was introduced to improve the performance of neural networks by allowing them to focus on specific parts of the input sequence when making predictions.
Here’s an overview of attention mechanisms:
I. Motivation:
- In traditional neural networks, such as recurrent neural networks (RNNs) or long short-term memory networks (LSTMs), the entire input sequence is usually processed to produce a fixed-size representation. This can be inefficient for tasks where different parts of the input have varying importance.
- Attention mechanisms aim to address this issue by enabling the model to selectively focus on certain parts of the input sequence, giving more weight to relevant information.
II. Basic Idea:
- Instead of encoding the entire input sequence into a fixed-size representation, attention mechanisms allow the model to dynamically choose which parts of the input to focus on during each step of the computation.
- The attention mechanism assigns different weights to different elements of the input sequence, emphasizing more on important…
