Question 8
In the context of Convolutional Neural Networks (CNNs), what is the primary challenge associated with backpropagation compared to traditional feedforward neural networks?
The increased number of parameters and layers in CNNs make
backpropagation computationally intensive and prone to overfitting.The presence of convolutional and pooling layers in CNNs requires the development of specialized backpropagation algorithms for efficient gradient computation.
The non-linear activation functions used in CNNs introduce discontinuities in the error surface, making it difficult to find the global minimum during backpropagation.
The spatial structure of CNNs results in weight sharing and local connectivity, requiring careful consideration of the error propagation process during backpropagation.