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Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation
arXiv – CS AI|Justus Westerhoff, Golzar Atefi, Mario Koddenbrock, Alexei Figueroa, Alexander L\"oser, Erik Rodner, Felix A. Gers||3 views
🤖AI Summary
Researchers propose a new IMPRINT framework for transfer learning that improves foundation model adaptation to new tasks without parameter optimization. The framework identifies three key components and introduces a clustering-based variant that outperforms existing methods by 4%.
Key Takeaways
- →The IMPRINT framework systematizes transfer learning imprinting into three components: generation, normalization, and aggregation.
- →Using multiple proxies to represent novel data in the generation step provides significant benefits.
- →Proper normalization is crucial for effective transfer learning performance.
- →A novel clustering-based variant motivated by neural collapse phenomenon outperforms previous methods by 4%.
- →The research provides the first connection between imprinting techniques and neural collapse theory.
#transfer-learning#foundation-models#neural-collapse#imprinting#clustering#machine-learning#ai-research#model-adaptation
Read Original →via arXiv – CS AI
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