The Inception module is built on a few design principles. It uses 1×1 convolutions to cheaply raise or lower the number of channels (the channel dimension). It runs convolutions of several window sizes—1×1, 3×3, and 5×5—in parallel on the same input, so features at different spatial scales are extracted at once, yielding a richer representation. It also approximates a sparse connectivity structure with dense, grouped components, which speeds up convergence. Separating the branches by filter size groups highly correlated features together within each branch; since training ultimately aims to extract independent features, clustering correlated features in advance helps the network converge faster.