Summary of Stochastic Subnetwork Annealing: a Regularization Technique For Fine Tuning Pruned Subnetworks, by Tim Whitaker et al.
Stochastic Subnetwork Annealing: A Regularization Technique for Fine Tuning Pruned Subnetworks
by Tim Whitaker, Darrell Whitley
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores innovative pruning methods to shrink deep neural networks’ size and computational complexity. Recent advancements have shown that removing large numbers of parameters from trained models can be done with minimal loss in accuracy, as long as a few continued training epochs are used. However, rapid removal of too many parameters often leads to an initial accuracy drop, compromising convergence quality. To mitigate this issue, iterative pruning approaches gradually remove small numbers of parameters over multiple epochs. Nevertheless, this still risks creating subnetworks that overfit local regions of the loss landscape. The authors introduce Stochastic Subnetwork Annealing, a novel regularization technique that represents subnetworks using stochastic masks. Each parameter has a probabilistic chance of being included or excluded on any given forward pass, allowing for smoother optimization at high levels of sparsity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes deep neural networks smaller and more efficient without losing their ability to learn. Usually, when we remove many parameters from these networks, it takes a few retraining sessions before they work just as well again. However, if we remove too many at once, it can make them perform worse temporarily. To solve this problem, researchers have developed ways to gradually remove small numbers of parameters over time. But even with this approach, the new network might still learn too much about its training data and not enough about general patterns. The authors propose a new technique called Stochastic Subnetwork Annealing that makes networks smaller in a way that’s easy for them to adapt to. |
Keywords
* Artificial intelligence * Optimization * Pruning * Regularization
