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About this paper
Raphaël Achddou, J.Matias di Martino, Guillermo Sapiro
Nested Learning presents a deep neural network framework that enforces hierarchical information extraction and calibrated confidence across multiple granularities. It creates models robust to distortions and adversarial attacks, achieving higher accuracy on degraded inputs and requiring over twice the adversarial noise to reach equivalent error rates compared to standard models.
Key insights
- Strictly nested labels and information bottlenecks, guided by information theory, provide a principled way to enforce hierarchical feature learning.
- Skipped connections are fundamental for acquiring new information at finer levels of granularity, counteracting the data-processing inequality.
- Calibrating confidence at each hierarchical level enables a more nuanced, 'human-like' inference, where the model can provide a confident coarse answer when fine details are ambiguous.
Summary by alphaXiv.
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