University of Michigan - Ann Arbor

Google

The ResNeXt architecture addresses the trade-off between nonlinearity and dimensionality in standard ResNet designs. Instead of increasing network depth or widening convolutions, ResNeXt increases the number of channels that carry information between blocks while avoiding a quadratic computational penalty. Inspired by the Inception block's strategy of separating information flow into independent groups, ResNeXt applies the exact same transformation across all of its parallel branches. This uniform multi-branch design minimizes the need for manual hyperparameter tuning for each branch.

ResNeXt Architecture

The ResNeXt block is a residual block design that incorporates grouped convolutions to process information efficiently across independent branches. It consists of a $$3 \times 3$$ grouped convolution sandwiched between two $$1 \times 1$$ convolutions. The first $$1 \times 1$$ convolution reduces the channel dimensionality, while the second $$1 \times 1$$ convolution restores it, effectively exchanging information across the $$g$$ independent groups. This design minimizes computational overhead: the network only pays an $$\mathcal{O}(c \cdot b)$$ cost for the $$1 \times 1$$ kernels and an $$\mathcal{O}(b^2 / g)$$ cost for the $$3 \times 3$$ grouped kernels, where $$c$$ is the number of channels and $$b$$ is the number of intermediate bottleneck channels. Similar to standard residual blocks, a $$1 \times 1$$ convolution can be applied to the residual connection to match dimensions if needed.

ResNeXt Block Structure

The AnyNet design space provides a generic architectural template for constructing and exploring families of convolutional neural networks. This macro-structure is composed of three primary components: a stem that performs the initial image processing, a body that carries out the bulk of the transformations to build object representations across multiple stages, and a head that converts these representations into the final desired outputs (e.g., via a softmax regressor). Within this generic framework, design choices such as stage depth, channel counts, and block structures (often using ResNeXt blocks) parameterize a vast space of potential network configurations.

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