Learning from Positive and Unlabeled Data with Adversarial Training
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples. This paper shows that the GAN (Generative Adversarial Networks) style of adversarial training is quite suitable for PU learning. GAN learns a generator to generate data (e.g., images) to fool a discriminator which tries to determine whether the generated data belong to a (positive) training class. PU learning is similar and can be naturally casted as trying to identify (not generate) likely positive data from the unlabeled set also to fool a discriminator that determines whether the identified likely positive data from the unlabeled set (U) are indeed positive (P). A direct adaptation of GAN for PU learning does not produce a strong classifier. This paper proposes a more effective method called Predictive Adversarial Networks (PAN) using a new objective function based on KL-divergence, which performs much better.~Empirical evaluation using both image and text data shows the effectiveness of PAN.
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