Cardinality vs width in the ResNext architecture I was recently reading the paper Aggregated Residual Transformations for Deep Neural Networks. 

One thing the author mentions in Section (5.1) is that increasing the cardinality (or, the number of branches), decreases validation error more than increasing the bottleneck width or increasing the depth. I understand the depth part, but I'm a bit confused about the width. Isn't the cardinality of a residual block the same as the bottleneck width? If not, what is the difference?

Thanks!
Like in InceptionNet, which increases width, the different paths are depth-concatenated as well as each path is different (i.e different filter sizes for different paths).

While in ResNeXt, which increases cardinality, the different paths are merged by adding them together, and each path is similar.

Read this article for more information An Overview of ResNet and its Variants