On the Power of Deep but Naive Partial Label Learning
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Partial label learning (PLL) is a class of weakly supervised learning where each training instance consists of a data and a set of candidate labels containing a unique ground truth label. To tackle this problem, a majority of current state-of-the-art methods employs either label disambiguation or averaging strategies. So far, PLL methods without such techniques have been considered impractical. In this paper, we challenge this view by revealing the hidden power of the oldest and naivest PLL method when it is instantiated with deep neural networks. Specifically, we show that, with deep neural networks, the naive model can achieve competitive performances against the other state-of-the-art methods, suggesting it as a strong baseline for PLL. We also address the question of how and why such a naive model works well with deep neural networks. Our empirical results indicate that deep neural networks trained on partially labeled examples generalize very well even in the over-parametrized regime and without label disambiguations or regularizations. We point out that existing learning theories on PLL are vacuous in the over-parametrized regime. Hence they cannot explain why the deep naive method works. We propose an alternative theory on how deep learning generalize in PLL problems.
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