Contents
Why is dot product used in attention?
As nbro wrote in its comment, the dot product is used to compute a sort of similarity score between the query and key vectors. Indeed, the authors used the names query, key and value to indicate that what they propose is similar to what is done in information retrieval.
What is additive attention?
Additive Attention, also known as Bahdanau Attention, uses a one-hidden layer feed-forward network to calculate the attention alignment score: f a t t ( h i , s j ) = v a T tanh where and are learned attention parameters. Here refers to the hidden states for the encoder, and is the hidden states for the decoder.
How does additive attention work?
Bahdanau attention mechanism It is also known as Additive attention as it performs a linear combination of encoder states and the decoder states. The attention mechanism aligns the input and output sequences, with an alignment score parameterized by a feed-forward network.
What is attention in CNN?
In the context of neural networks, attention is a technique that mimics cognitive attention. The effect enhances the important parts of the input data and fades out the rest—the thought being that the network should devote more computing power on that small but important part of the data.
Does LSTM use attention?
At both the encoder and decoder LSTM, one Attention layer (named “Attention gate”) has been used. So, while encoding or “reading” the image, only one part of the image gets focused on at each time step. And similarly, while writing, only a certain part of the image gets generated at that time-step.
What is the difference between dot-product attention and multi-head attention?
It contains blocks of Multi-Head Attention, while the attention computation itself is Scaled Dot-Product Attention. where dₖ is the dimensionality of the query/key vectors. The scaling is performed so that the arguments of the softmax function do not become excessively large with keys of higher dimensions.
How are the different forms of attention used?
The Transformer uses word vectors as the set of keys, values as well as queries. It contains blocks of Multi-Head Attention, while the attention computation itself is Scaled Dot-Product Attention.
How does multi-head attention reduce the set of equations?
If we compute alignment using basic dot-product attention, the set of equations used to calculate context vectors can be reduced as follows. Multi-head attention takes this one step further.
How are attention distributions used in a model?
Such attention distributions also help provide a degree of interpretability for the model. Given a query q and a set of key-value pairs (K, V), attention can be generalised to compute a weighted sum of the values dependent on the query and the corresponding keys.