In the context of transformers, pooling refers to the process of summarizing the outputs of the transformer layers into a fixed-size vector, often used for downstream tasks such as classification.
In a transformer architecture, the input sequence is processed by a series of self-attention and feedforward layers. Each layer produces a sequence of output vectors, which encode the input sequence in a higher-level representation. Pooling involves taking the output vectors from one or more of these layers and aggregating them into a single vector.
There are different types of pooling mechanisms used in transformer architectures, including:
Max Pooling: where the maximum value across the sequence of output vectors is selected as the summary representation.
Mean Pooling: where the average of the output vectors is taken as the summary representation.
Last Hidden State: where the final output vector of the transformer is used as the summary representation.
Self-Attention Pooling: where a weighted sum of the output vectors is computed, with the weights determined by a learned attention mechanism.
Overall, pooling is an important component of transformer architectures, as it allows for the extraction of a fixed-size representation of the input sequence, which can be used for a variety of downstream tasks.
