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Communication Dans Un Congrès Année : 2022

The Optimal Noise in Noise-Contrastive Learning Is Not What You Think

Résumé

Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning. Framing the problem as a self-supervised task, where data samples are discriminated from noise samples, is at the core of state-of-the-art methods, beginning with Noise-Contrastive Estimation (NCE). Yet, such contrastive learning requires a good noise distribution, which is hard to specify; domain-specific heuristics are therefore widely used. While a comprehensive theory is missing, it is widely assumed that the optimal noise should in practice be made equal to the data, both in distribution and proportion; this setting underlies Generative Adversarial Networks (GANs) in particular. Here, we empirically and theoretically challenge this assumption on the optimal noise. We show that deviating from this assumption can actually lead to better statistical estimators, in terms of asymptotic variance. In particular, the optimal noise distribution is different from the data's and even from a different family.
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Dates et versions

hal-03808351 , version 1 (12-10-2022)

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  • HAL Id : hal-03808351 , version 1

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Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen. The Optimal Noise in Noise-Contrastive Learning Is Not What You Think. UAI 2022 - 38th Conference on Uncertainty in Artificial Intelligence, Aug 2022, Eindhoven, Netherlands. ⟨hal-03808351⟩
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