Deep Learning · TEE · section

R26 Q3b

Re examination February 2026 fully solved

Question

Question (10 marks). How should CNN filter counts be selected? Explain effects on feature extraction, dimensionality, computation/memory, overfitting and hierarchical learning.

Answer — step by step

Choose filter counts as architecture hyperparameters using task complexity, available data, validation performance and resource limits. There is no universal correct count.

1. More filters can represent a wider variety of learned features at a layer; redundant filters do not automatically improve accuracy.

2. Cout filters produce Cout output channels; spatial height/width still follow kernel, padding and stride.

3. A K × K layer uses K²CinCout weights plus Cout biases. More output channels increase current activations and the input-channel cost of the next convolution.

4. Extra capacity can overfit limited data, so use validation, augmentation, transfer learning or regularization. Too few channels may underfit.

5. Later layers often use more channels after spatial downsampling, allowing complex feature combinations at a smaller spatial size. This is a common design choice, not a compulsory rule.

Example: a 3 × 3 layer with 16 input channels has (9 × 16+1) × 32 = 4,640 parameters for 32 filters, versus 9,280 for 64 filters. Output-channel count doubles while the same spatial-size formula applies.