AI0-001 AI Concepts and Techniques Practice Question
A research team is training a deep learning model for image classification using a small dataset of 1,000 labeled images. They are concerned about overfitting. Which combination of regularisation techniques would be MOST effective?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Dropout with a rate of 0.5 and L2 regularisation
Dropout randomly disables neurons during training to prevent co-adaptation, and L2 regularisation penalises large weights. Both are standard regularisation techniques. L1 promotes sparsity but is less common for dense layers. Batch normalisation helps convergence but is not primarily a regularisation method.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use early stopping without any other regularisation
Why it's wrong here
Early stopping alone may not sufficiently regularise a small dataset; combining with dropout or weight decay is more robust.
- ✓
Dropout with a rate of 0.5 and L2 regularisation
Why this is correct
Dropout and L2 regularisation together effectively reduce overfitting by preventing reliance on specific neurons and penalising large weights.
- ✗
L1 regularisation and batch normalisation
Why it's wrong here
L1 regularisation encourages sparse weights, which may not be ideal for dense layers, and batch normalisation is not a strong regulariser for small datasets.
- ✗
Increase learning rate and use momentum
Why it's wrong here
Increasing learning rate can cause divergence and does not address overfitting; momentum helps convergence but not regularisation.
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