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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 26·5 min read
Across the Loss Landscape with Progressive Growth
TL;DR
Progressive growth strategies in deep learning bias training towards flatter loss landscapes, enhancing generalization. This method, while effective in producing flatter solutions, does not guarantee improved test performance.
✦ Why It Matters
Consider implementing progressive growth strategies in your neural network training to explore flatter loss landscapes.
Key Takeaways
- Start training with a low-dimensional model to enhance optimization.
- Incrementally unlock random subspaces to bias towards flatter regions.
- Monitor the relationship between flatness and test performance closely.
- Experiment with progressive growth in your existing deep learning projects.
How It Works
The progressive growth strategy involves starting with a simplified model and gradually expanding its capacity. By freezing certain parameters while optimizing others, this method encourages exploration of flatter regions in the loss landscape, which are believed to improve generalization.
The researchers demonstrated that this approach effectively increases the likelihood of finding wider basins, which are less sensitive to perturbations in the input data.