Why do we increase dimensions in resnet-50 architecture?

Question

In rensnet-50's bottleneck block, we use conv1x1(previous block out,64), conv3x3(64,64), conv1x1(64,256) and then we repeat.

Why do we increase dimensions in the third conv1x1 layer only to reduce it in the next block?

Answer 1

It's a bottleneck layer, see the answer to this question:

https://ai.stackexchange.com/a/4887

and this question:

https://stats.stackexchange.com/questions/205150/how-do-bottleneck-architectures-work-in-neural-networks

In essence, it's to reduce the feature count, particularly for the 3x3 convolution.