What are the limits of convolutional neural networks?

What are the limits of convolutional neural networks?

Convolutional Neural Networks (CNNs) excel in image processing but have limitations like overfitting, lack of spatial reasoning, and difficulty with non-grid data.

Can you explain overfitting in CNNs?

Overfitting occurs when a CNN learns the training data too well, capturing noise instead of general patterns, leading to poor performance on unseen data.

What about their performance on 3D data?

CNNs struggle with 3D data because they are designed for 2D spatial hierarchies. 3D CNNs exist but are more complex and computationally intensive.

Are there alternatives to CNNs for image processing?

Yes, alternatives include Vision Transformers (ViTs) and traditional methods like SIFT or HOG, which can be effective depending on the task.

How do CNNs compare to Vision Transformers?

CNNs focus on local patterns through convolutional layers, while Vision Transformers use self-attention to capture global context, often leading to better performance on large datasets.