A **Pyramidal Bottleneck Residual Unit** is a type of residual unit where the number of channels gradually increases as a function of the depth at which the layer occurs, which is similar to a pyramid structure of which the shape gradually widens from the top downwards. It also consists of a bottleneck using 1x1 convolutions. It was introduced as part of the [PyramidNet](https://paperswithcode.com/method/pyramidnet) architecture.

**DLA**, or **Deep Layer Aggregation**,  iteratively and hierarchically merges the feature hierarchy across layers in neural networks to make networks with better accuracy and fewer parameters. 

In iterative deep aggregation (IDA), aggregation begins at the shallowest, smallest scale and then iteratively merges deeper,
larger scales. In this way shallow features are refined as
they are propagated through different stages of aggregation.

In hierarchical deep aggregation (HDA), blocks and stages
in a tree are merged to preserve and combine feature channels. With
HDA shallower and deeper layers are combined to learn
richer combinations that span more of the feature hierarchy.
While IDA effectively combines stages, it is insufficient
for fusing the many blocks of a network, as it is still only
sequential.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com