What are the limitations of CNN?

What are the limitations of CNNs in image processing?

CNNs can struggle with small datasets, require large amounts of labeled data, and may not generalize well to unseen data.

Can CNNs handle different image sizes?

CNNs typically require fixed-size inputs, so images need to be resized or padded, which can lead to loss of information.

What about their performance on non-image data?

CNNs are primarily designed for grid-like data, so they may not perform well on sequential or non-grid data without modifications.

Are there alternatives to CNNs for image tasks?

Yes, alternatives like Vision Transformers (ViTs) and traditional machine learning methods can be used depending on the task.

How do CNNs compare to other neural networks?

CNNs excel in spatial hierarchies for images, but other architectures like RNNs or Transformers may outperform them in different contexts.