Overview
The degradation problem in deep networks is a significant challenge that arises when adding more layers leads to increased training error. This counterintuitive phenomenon can hinder the performance of neural networks, making it crucial for practitioners to understand its implications. Solutions suc...
Key Terms
Example: A neural network can be used for image classification.
Example: A model that performs well on training data but poorly on unseen data is overfitting.
Example: In deep networks, early layers may not learn effectively due to vanishing gradients.
Example: Residual networks have been successful in image recognition tasks.
Example: Batch normalization can help mitigate the degradation problem.
Example: A low training error suggests that the model has learned the training data well.