AI0-001 Machine Learning and Deep Learning Practice Question
A healthcare startup is developing a diagnostic system using medical images. The team has collected 10,000 labeled images of skin lesions. They plan to train a convolutional neural network (CNN) from scratch. However, training converges slowly, and the validation accuracy plateaus at 70%. The data scientist suspects overfitting. The dataset contains 8,000 images of benign lesions and 2,000 of malignant. The team has limited GPU resources. Which of the following is the MOST effective course of action to improve validation accuracy? A. Reduce the number of convolutional layers. B. Apply transfer learning using a pre-trained model on ImageNet. C. Increase the learning rate by a factor of 10. D. Add more dropout after every convolutional layer.
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
✓
Apply transfer learning using a pre-trained model on ImageNet.
Transfer learning leverages a model pre-trained on a large dataset (e.g., ImageNet), which provides useful features for medical images and reduces the need for large amounts of data and computational resources. It is particularly effective when the dataset is small and imbalanced. Option A (increasing learning rate) might cause divergence or overshoot minima. Option B (reducing layers) may reduce capacity and underfit. Option C (adding dropout) can help with overfitting but is unlikely to jump from 70% to a significantly higher accuracy given limited data; transfer learning provides a stronger boost.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the learning rate by a factor of 10.
Why it's wrong here
Increasing learning rate too much can cause the optimizer to overshoot and fail to converge.
- ✗
Reduce the number of convolutional layers.
Why it's wrong here
Reducing layers reduces model capacity, which may lead to underfitting and lower performance.
- ✗
Add more dropout after every convolutional layer.
Why it's wrong here
While dropout regularizes, it may not be sufficient to overcome overfitting from a small dataset; transfer learning is more impactful.
- ✓
Apply transfer learning using a pre-trained model on ImageNet.
Why this is correct
Transfer learning provides a strong feature extractor learned from a large dataset, which can significantly improve performance with limited data.
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