Propose a technique or modification to your existing CNN architecture to encourage the model to learn meaningful, low-dimensional features rather than relying on trivial patterns. Discuss the tradeoff between this approach and traditional methods of regularization (e.g., dropout, weight decay).

Implement an autoencoder as a pretraining step for the CNN, forcing it to learn low-dimensional representations that capture underlying structure in the data. This can be done using techniques like sparse coding or contractive autoencoders.

Tagged 'image_classification'], 'pretraining'], 'representation_learning'

Propose a technique or modification to your existing CNN architecture to encourage the model to learn meaningful, low-dimensional features rather than relying on trivial patterns. Discuss the tradeoff between this approach and traditional methods of regularization (e.g., dropout, weight decay).

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