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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|March 4, 2026 at 05:00 AM|1 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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