Deep mutual GANs: representation learning from multiple experts

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Representation learning is one of the canonical objectives of most deep learning models. However, the learning of real-world clinical data is often compromised by their inherently imbalanced or long-tailed distribution wherein a few classes have significantly larger numbers of training instances than do the other classes. In this study, we investigated the representation learning of such long-tailed data distributions by the use of a deep mutual ensemble generative adversarial network. Our proposed framework consists of multiple powerful pre-trained discriminator networks that transfer knowledge to multiple individual untrained generator networks. During the training process, each generator learns to collaborate with the other generators. Additionally, each generator receives feedback from the individual discriminators in an adversarial manner. Especially, we explored the use of mutual information shared between the independent generators that makes our framework robust against misclassification of long-tailed data distributions in medical image analysis. We evaluated our proposed framework on four public datasets that represented different medical imaging modalities and imbalance ratios. Our experimental results show that our proposed framework benefits from ensemble learning and shared mutual learning, and achieves compelling results on several medical imaging benchmarks. Thus, our approach offers potential advantages over traditional deep learning in real-world applications.