# F23 Q5a

Subject: Deep Learning

Exam: MT2

Reader: https://deep-learning-study-room.vercel.app/#mt2/read/13/mt2-c13-s4

Canonical: https://deep-learning-study-room.vercel.app/read/dl/mt2/section/mt2-c13-s4/

## Question

**Question (10 marks).** For Inception V 1, justify different kernel sizes in one block, explain 1 × 1 convolutions and sketch one block.

## Answer — step by step

An Inception module captures features at multiple scales in parallel. A typical module has a 1 × 1 branch, a 1 × 1 reduction followed by 3 × 3 convolution, a 1 × 1 reduction followed by 5 × 5 convolution, and a pooling branch followed by 1 × 1 projection. Padding and strides preserve compatible spatial sizes so branch outputs can be concatenated along the channel axis.

![Parallel branches preserve height and width then concatenate channels](https://deep-learning-study-room.vercel.app/assets/figures/inception.svg)

Diagram: Parallel branches preserve height and width then concatenate channels. Labels: Input | 1 × 1 | 1 × 1 reduce | 3 × 3 | 1 × 1 reduce | 5 × 5 | 3 × 3 pool | 1 × 1 | Concatenate channels | All branch height/width values must match.

Parallel branches preserve height and width then concatenate channels Tap the diagram to enlarge.

A 1 × 1 convolution mixes input channels at each position. With fewer output channels, it forms a bottleneck before an expensive larger kernel. ReLU following it adds nonlinearity. This reduces the number of input channels processed by 3 × 3 or 5 × 5 kernels while preserving useful learned combinations.

For H × W spatial size, Cin input channels and Cout output filters, a K × K convolution uses H × W × Cin × Cout × K² multiply-accumulates when the output spatial size stays H × W. A bottleneck changes the calculation to H × W × Cin × r + H × W × r × Cout × K². A multiply-accumulate is counted as one MAC here; some FLOP conventions count it as two operations. The benefit depends on the selected bottleneck width r. Compared with AlexNet's sequential kernels, Inception explicitly combines local and larger-scale features within one module.