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cloud.google.com·6h ago
Data Science
Towards Data Science·Sep 9·5 min read
The Symmetry That Breaks Neural Network Averaging
TL;DR
Permutation symmetry in deep learning affects weight averaging and model merging, revealing that naive averaging can lead to suboptimal performance. Understanding this symmetry is crucial for improving model integration techniques.
✦ Why It Matters
Reevaluate your model merging strategies to ensure they account for permutation symmetry for better performance.
Key Takeaways
- Always align models before averaging their weights.
- Use representation similarity metrics instead of weight-space distance.
- Check interpolation curves for model soups before averaging.
- Employ tools like Git Re-Basin for model merging.
How It Works
Neural networks learn their own basis functions, leading to permutation symmetry where the order of neurons does not affect the output. This results in multiple equivalent solutions in the loss landscape, complicating weight averaging.
By aligning models based on their neuron activations, one can effectively merge their weights without losing performance.