The big idea: A convolutional neural network exploits two facts a plain network ignores: nearby pixels belong together, and a feature is the same wherever it appears.
A cat's ear is a cat's ear in the top-left or the bottom-right. A CNN learns it once.
Why a plain network struggles on images: Flattening a 200×200 colour image gives 120,000 inputs. Connecting those to even 1,000 hidden units is 120 million weights in the first layer alone.
Worse, flattening destroys the geometry: the network has no idea which pixels were adjacent, so it must learn every feature separately in every position.
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1
Convolution
2
Shared weights
3
Pooling
4
Stack and repeat
| Plain network | CNN | |
|---|---|---|
| Input | Flattened — geometry lost | Keeps the 2D structure |
| Weights in layer 1 | One per pixel per unit — millions | One small filter, reused |
| A feature moved | Must be relearned in the new position | Already recognised — filters are shared |
| What layers learn | No inherent hierarchy | Edges → shapes → objects |
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Strong at
- Images, video, and anything with local structure
- Also audio spectrograms and some text
- Recognising a feature wherever it appears
Costs
- Needs large labelled datasets
- Training needs accelerators — days on a GPU
- Many hyperparameters to choose
Fails at
- Data with no spatial meaning — a table of unrelated columns
- Explaining itself, exactly like any network
- Objects at angles it never saw during training
Transfer learning is the practical answer: Training a CNN from scratch needs data and hardware most people do not have. In practice you take a network already trained on millions of general images, keep its learned feature detectors, and retrain only the final layer on your own few thousand examples.
Saying this shows you know how CNNs are actually used.
How this is tested — you must explain convolution and shared weights, and say what they buy over a plain network. It comes up two ways:
Paper 1 Section A
- Describe how a CNN learns spatial hierarchies, 4-5 marks
- State what a filter does
- Explain the purpose of pooling
Paper 1 Section B — case study
- Justify a CNN for a scenario
- Explain why a plain network is unsuitable
The classic trap: Describing a CNN as "a neural network for images" and stopping. The marks are for convolution (a small filter slid across), shared weights (the same filter everywhere) and pooling — the three things that make it different.
IB-style questionDescribe[6 marks]
A hospital wants to detect tumours in X-ray images. Describe how a CNN suits this better than a plain neural network, and state one practical difficulty.
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