# Model answer: AlexNet, Inception and ResNet

Subject: Deep Learning

Exam: MT2

Reader: https://deep-learning-study-room.vercel.app/#mt2/read/8/mt2-c8-s8

Canonical: https://deep-learning-study-room.vercel.app/read/dl/mt2/section/mt2-c8-s8/

## Question

**Question.** Compare the main ideas of AlexNet, Inception and ResNet. Explain how to recognize their architecture diagrams.

## Answer — step by step

All three are CNN architectures, but their characteristic structures differ.

![Each block illustrates the defining design idea; the full architecture diagrams give its detailed layout.](https://deep-learning-study-room.vercel.app/assets/figures/v_architectures.svg)

Diagram: Each block illustrates the defining design idea; the full architecture diagrams give its detailed layout.. Labels: Three architecture ideas to remember | Three architecture ideas to remember | These are different designs, not three consecutive layers of one network. | AlexNet | Sequential | convolution and | pooling stages | Inception | Parallel branches; | concatenate their | channels | ResNet | Learn a branch; add a | shortcut | A diagram should show the mechanism, not only the architecture name.

Each block illustrates the defining design idea; the full architecture diagrams give its detailed layout. Tap the diagram to enlarge.

| Architecture | Main idea | What to show in a diagram |
| --- | --- | --- |
| AlexNet | Successive convolutional feature extraction followed by dense classification | Five convolutional layers and three fully connected layers, with the stated pooling/normalization stages |
| Inception v1 | Parallel branches analyze features at different spatial scales | 1 × 1, reduced 3 × 3, reduced 5 × 5 and pooling branches joined by channel concatenation |
| ResNet | Learn a residual transformation and add a shortcut | A transformation branch and input shortcut meeting at a plus sign |

AlexNet's exam-relevant features include ReLU, GPU-based training, augmentation and dropout in the fully connected part. A parameter calculation must follow the given channel connectivity; a grouped implementation and a simplified fully connected-channel diagram need not have the same count.

Inception uses 1 × 1 convolutions to mix channels and, when a smaller number of filters is chosen, reduce the channel depth before expensive spatial filters. Branch outputs must have compatible height and width for concatenation. Concatenation adds channel counts by joining their lists.

ResNet computes a learned transformation F(x) and combines it with the shortcut:



$$
H(x)=F(x)+x
$$



This is entrywise addition. The two shapes must match; a projection can adapt a shortcut when they do not. The shortcut supplies an additional route for information and gradients and makes identity-like mappings easy to represent.

**Key distinction:** an Inception join concatenates feature maps; a residual join adds corresponding values. Their diagram symbols and output-channel calculations must therefore differ.