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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 12·5 min read
Investigating catastrophic forgetting in sound event classification
TL;DR
This research explores methods to mitigate catastrophic forgetting in sound event classification during class incremental learning. Key findings indicate that freezing the feature extractor while fine-tuning a dynamic classifier head significantly reduces forgetting and enhances training stability.
✦ Why It Matters
Consider implementing a dynamic classifier head with a frozen feature extractor in your sound classification models today.
Key Takeaways
- Implement class incremental learning to enhance model adaptability.
- Use FSD50K and AudioSet datasets for sound classification tasks.
- Freeze feature extractors to minimize forgetting during training.
- Fine-tune dynamic classifier heads for improved training stability.
How It Works
The proposed method involves freezing the feature extractor of the neural network, which prevents its weights from being updated during training on new tasks. Instead, a dynamic classifier head is fine-tuned, allowing the model to adapt to new classes while retaining knowledge of previously learned classes.
This selective weight protection helps maintain performance across tasks.