ResNeXt Block
ResNeXt Block
A **ResNeXt Block** is a type of [residual block](https://paperswithcode.com/method/residual-block) used as part of the [ResNeXt](https://paperswithcode.com/method/resnext) CNN architecture. It uses a "split-transform-merge" strategy (branched paths within a single module) similar to an [Inception module](https://paperswithcode.com/method/inception-module), i.e. it aggregates a set of transformations. Compared to a Residual Block, it exposes a new dimension,  *cardinality* (size of set of transformations) $C$, as an essential factor in addition to depth and width. 

Formally, a set of aggregated transformations can be represented as: $\mathcal{F}(x)=\sum_{i=1}^{C}\mathcal{T}_i(x)$, where $\mathcal{T}_i(x)$ can be an arbitrary function. Analogous to a simple neuron, $\mathcal{T}_i$ should project $x$ into an (optionally low-dimensional) embedding and then transform it.
Aggregated Residual Transformations for Deep Neural Networks
https://paperswithcode.com/paper/aggregated-residual-transformations-for-deep
Aggregated Residual Transformations for Deep Neural Networks