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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 25·5 min read
Complexity Induction: Compositional Generalization via Structured Label Distortion
TL;DR
Structured distortion of training data, termed complexity induction, enhances compositional generalization in CNN classifiers without altering their architecture. This method allows models to predict unseen class combinations effectively.
✦ Why It Matters
Experiment with structured label distortion to enhance your CNN models' ability to generalize to unseen classes.
Key Takeaways
- Implement structured label distortion in your training data.
- Explore mixed labels to enhance model embedding structures.
- Use expanded datasets to improve factorization in CNNs.
- Test the impact of structured versus random labels on performance.
How It Works
Complexity induction leverages structured distortions in training data to enhance a CNN's ability to generalize. By using mixed labels, the model learns to recognize inter-class overlaps, while expanded datasets introduce false samples that help refine the model's internal representations.
This dual approach allows the CNN to better understand and predict unseen class combinations.