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cloud.google.com·7h ago
AI Research
arXiv CS.AI·10d ago·5 min read
Removing spurious minima for planar features by skip connections
TL;DR
A learned linear skip connection in shallow ReLU networks effectively eliminates spurious local minima in the loss landscape, enhancing feature learning. This approach ensures that student networks can learn the essential features of teacher networks without being hindered by local minima.
✦ Why It Matters
Implement learned skip connections in your neural networks to avoid spurious minima and improve training outcomes.
Key Takeaways
- Incorporate skip connections to enhance feature learning.
- Ensure student networks are at least as wide as teacher networks.
- Understand the impact of overparameterization on training.
- Focus on the span of teacher features for effective learning.
How It Works
The study introduces a learned linear skip connection that allows the student network to bypass certain local minima in the loss landscape. By ensuring that the student network's width matches or exceeds that of the teacher network, the skip connection effectively removes spurious minima, allowing for more reliable convergence to optimal solutions.
This mechanism leverages the structure of the loss landscape to facilitate better feature learning.