CNN Training and Optimization 5

Question 1

What is 'early stopping' in CNN training and what metric is typically monitored?

Accepted Answer

Halting training when validation loss stops improving for a set number of epochs

Answer

Early stopping halts training when the validation loss (or other metric) fails to improve for a specified patience period, preventing overfitting by avoiding unnecessary epochs.

Question 2

Which CNN training phenomenon occurs when a model performs well on training data but poorly on unseen test data?

Accepted Answer

Overfitting

Answer

Overfitting occurs when the model memorizes training data patterns rather than learning generalizable features, resulting in high training accuracy but poor test performance.

Question 3

What does 'test-time augmentation' (TTA) do to improve CNN prediction performance?

Accepted Answer

Applies multiple augmentations to test images and averages the predictions

Answer

TTA generates multiple augmented versions of each test image, passes them through the model, and averages the predictions to reduce variance and improve accuracy.

Question 4

In deep CNN training, what is 'residual learning' (as in ResNets) and how does it aid optimization?

Accepted Answer

Learning residual functions with reference to layer inputs via skip connections, easing gradient flow

Answer

Residual learning reformulates layers to learn residual functions F(x) added back to the input x, making identity mappings easy and allowing gradient to flow directly through skip connections to earlier layers.

Question 5

What is 'Nesterov Accelerated Gradient' (NAG) and how does it differ from standard momentum SGD?

Accepted Answer

NAG computes the gradient at the lookahead position (after the momentum step) rather than the current position

Answer

NAG evaluates the gradient at the predicted future position (current weights plus momentum step) rather than the current position, providing a more informed and corrective update.

CNN - Convolutional Neural Networks Practice Test

CNN - Convolutional Neural Networks Practice Test

CNN Training and Optimization 5