Is Discriminator a Good Feature Extractor?
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Discriminator from generative adversarial nets (GAN) has been used by some research as feature extractor in transfer learning and worked well. But there are also some studies believed that this is a wrong research direction because intuitively the task of discriminator focuses on separating the real samples from the generated ones, making the feature extracted in this way useless for most of the downstream tasks. In this work, we find that the connection between the task of discriminator and the feature is not as strong as people thought, that the main factor restricting the feature learned by the discriminator is not the task of the discriminator itself, but the need to prevent the entire GAN model from mode collapse during the training. From this perspective and combined with further analyses, we find that to avoid mode collapse in the training process of GAN, the features extracted by the discriminator is not guided to be different for the real samples, but divergence without noise is indeed allowed and occupies a large proportion of the feature space. This makes the features learned more robust and helps answer the question about why discriminator can succeed as feature extractor in the related research. After these, we analyze the counterpart of the discriminator extractor, the classifier extractor that assigns the target samples to different categories. We find the performance of the discriminator extractor may be inferior to classifier based extractor when the source classification task is similar to the target task, which is a common case. But the ability to avoid noise prevents discriminator from being replaced by classifier. Last but not least, as our research also reveals a ratio playing an important role in GAN's training to prevent mode collapse, it may contribute to the basic GAN study.
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