The burgeoning complexity of neural networks has compelled the pursuit of methods to reduce their operational demands. Castells & Yeom, 2021 introduce an automatic pruning method which efficiently preserves model accuracy by learning which neurons to retain. The method makes use of a trainable bottleneck and has shown promising results in various architectures and data sets.

Essential Advancements:

• Prunes filters based on a newly introduced trainable bottleneck.
• Requires only one finetuning epoch without significant data set loss.
• Outperforms existing pruning methods after finetuning.

This method elevates the efficiency of neural network operation while maintaining accuracy, representing a significant step in optimizing AI models for practical, resource-limited scenarios.