The attention mechanism in deep learning is a technique that enables models to focus on specific parts of their input, much like how humans pay attention to particular aspects of their environment.

An attention mechanism is a technique used in machine learning and artificial intelligence to improve the performance of models by focusing on relevant information. It allows models to selectively attend to different parts of the input data, assigning varying degrees of importance or weight to different elements.

The attention mechanism of BERT works as Query (Q), Key (K), and Value (V) that start a linear transformation to “dynamically” generate weights for different connections, and then feed them into the scaling dot product. In the definition of self-attention, Q is K itself. d_k is the dimension of Q and K.

In a vision network, an attention mechanism is essentially a dynamic weight adjustment function based on an attention function g(x) and an input feature map x that is superimposed between the convolutional layers. Its role is to tell the next layer of the deep network which features are more or less important.