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What is: ResNeXt?

SourceAggregated Residual Transformations for Deep Neural Networks
Year2000
Data SourceCC BY-SA - https://paperswithcode.com

A ResNeXt repeats a building block that aggregates a set of transformations with the same topology. Compared to a ResNet, it exposes a new dimension, cardinality (the size of the set of transformations) CC, as an essential factor in addition to the dimensions of depth and width.

Formally, a set of aggregated transformations can be represented as: F(x)=i=1CTi(x)\mathcal{F}(x)=\sum_{i=1}^{C}\mathcal{T}_i(x), where Ti(x)\mathcal{T}_i(x) can be an arbitrary function. Analogous to a simple neuron, Ti\mathcal{T}_i should project xx into an (optionally low-dimensional) embedding and then transform it.