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About this paper
Saeed Ebrahimi, Sahar Rahimi, Ali Dabouei, Srinjoy Das, Jeremy M. Dawson, Nasser M. Nasrabadi
The GIF framework introduces a generative approach to large-scale face recognition training by converting atomic scalar labels into structured identity codes. This method reduces computational complexity from linear O(m) to logarithmic O(log(m)) with respect to the number of identities, while also mitigating the "minority collapse" problem in imbalanced datasets.
Key insights
- The framework successfully transforms computational complexity from O(m) to O(log(m)) with respect to identity count, enabling training on datasets with millions of identities.
- Decoupling code organization from sample distribution effectively resolves the minority collapse problem, ensuring robust performance across all identities regardless of their frequency.
- The shift from traditional classification to a generative modeling paradigm proves highly effective for large-scale face recognition.
Summary by alphaXiv.
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