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GoogLeNet/Inception: multi-scale branches with bottleneck projections

Contribution grade

Efficient convolutional visual architecture · Human-designed research method or resource

Reference score180,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Combined parallel convolution/pooling branches and1×1channel-reduction projections in repeated Inception modules, balancing network width/depth and computation in GoogLeNet.

Scientific scope

Combined parallel convolution/pooling branches and1×1channel-reduction projections in repeated Inception modules, balancing network width/depth and computation in GoogLeNet. Distinct from formal Inception-v3's later factorization/regularization refinements. Inherits NetworkInNetwork1×1convolutions and prior multi-scale reasoning; source explicitly treats its sparse/Hebbian design motivation as unverified, not a proved automatic optimal architecture.22layer entry and ensemble gains are benchmark/protocol specific.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Distinct from formal Inception-v3's later factorization/regularization refinements. Inherits NetworkInNetwork1×1convolutions and prior multi-scale reasoning; source explicitly treats its sparse/Hebbian design motivation as unverified, not a proved automatic optimal architecture.22layer entry and ensemble gains are benchmark/protocol specific.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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