Semi-Supervised Learning with Generative Adversarial Networks (1606.01583v2)

Abstract: We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN.

• The paper presents SGAN, a framework that extends GANs by training the discriminator to also classify real versus fake data.
• It demonstrates improved classification accuracy and clearer sample generation on reduced datasets compared to traditional GANs.
• The approach creates a feedback loop between generative and discriminative components, paving the way for more efficient semi-supervised learning.

Semi-Supervised Learning with Generative Adversarial Networks

This paper presents a novel adaptation of Generative Adversarial Networks (GANs) to the domain of semi-supervised learning, introducing the concept of the Semi-Supervised GAN, or SGAN. The authors enhance the traditional GAN framework by modifying the discriminator network to output class labels, thereby integrating a classification task alongside the generative capabilities.

Core Contributions

The primary contributions of this research are threefold:

1. SGAN Framework: The introduction of SGAN allows for simultaneous learning of both generative models and classifiers. This extension to GANs leverages the discriminator to also serve as a classifier, outputting one of N+1 classes to account for true classes plus a fake class generated by the generator.
2. Performance Improvement: SGAN demonstrates improved classification performance on restricted datasets when compared to a baseline classifier with no generative component. The integration of D and C networks facilitates a feedback loop that enhances the accuracy of classification on reduced training samples.
3. Sample Quality and Training Efficiency: The use of SGAN significantly enhances the quality of generated samples and reduces the time required for training the generator. Generated samples from SGAN, particularly in tests on the MNIST dataset, were found to be notably clearer than those from standard GAN setups.

Experimental Findings

The research utilizes the MNIST dataset to evaluate the performance of SGAN. Comparisons between SGAN and traditional GAN show the superior clarity and quality of samples produced by SGAN. The classifier component in SGAN outperformed baseline classifiers under constrained conditions, as shown by the results detailed in Table 1, where SGAN maintains higher accuracy across various sample sizes.

Implications and Future Directions

The implications of SGAN are significant for both practical applications and theoretical advancements. The enhancement in sample quality and classifier accuracy suggests utility in applications involving limited labeled data. The feedback loop effectively couples generative and discriminative learning processes, potentially offering frameworks for more efficient learning in other conditional and semi-supervised domains.

Future research avenues proposed include exploring weight-sharing schemes between discriminator and classifier, generating labeled samples directly for increased control over classes, and integrating ladder networks to improve label efficiency.

Conclusion

This advancement in the design of GANs to support semi-supervised learning tasks exemplifies the potential for integrated generative-discriminative systems to improve data efficiency and model performance. Through innovative design and rigorous evaluation, SGAN contributes a meaningful extension to the capabilities of GANs, paving the way for further developments in AI methodologies leveraging semi-supervised learning paradigms.