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AI Research
arXiv CS.AI·1d ago·5 min read
Rethinking Activation Functions: Reducing Bias Can Improve Learning
TL;DR
Reducing activation bias in neural networks can enhance learning outcomes, leading to lower final loss despite slower initial progress. This finding challenges the conventional reliance on activation functions like ReLU and GELU, which often produce positive outputs.
✦ Why It Matters
Consider adjusting activation functions in your models to reduce bias for better long-term performance.
Key Takeaways
- Experiment with reducing activation bias in your models.
- Monitor initial learning rates versus final performance outcomes.
- Evaluate the impact of activation functions on loss landscapes.
- Incorporate findings into future model design and training strategies.
Full Summary
Activation functions like ReLU, GELU, and Sigmoid typically generate outputs that are predominantly positive, creating what is termed activation bias. This study investigates the impact of activation bias on learning dynamics using controlled models.
Results indicate that while activation bias can accelerate initial loss reduction, it also leads to overshooting and a steeper loss landscape. Conversely, reducing activation bias slows down early learning but ultimately results in a lower final loss.
Experiments conducted on larger vision and language models corroborate this trend, showing that smaller activation bias correlates with improved performance in later stages of training. These insights suggest that activation bias is a critical factor that should be explicitly considered in the design of future activation functions.