Propose a strategy for designing the architecture of your multi-layer autoencoder, taking into account the tradeoff between layer size and depth. Explain how this will affect the model’s ability to learn useful representations and generalize across different tasks.

Design a deep autoencoder with multiple layers in both encoder and decoder, but use early stopping or regularization techniques to prevent overfitting. This allows for more powerful representation learning while minimizing the risk of overfitting. The depth of the model can be increased gradually, adding more layers as needed, to improve representational power without sacrificing generalizability.