Deep Learning · MT2 · section
Model answer: AlexNet, Inception and ResNet
CNN architectures and transfer learning
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.
| 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)+xThis 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.