Semi Supervised Learning Using GANs

• SSL with GANs is found to be useful when doing classification with limited amount of labeled data.
• The unlabeled samples can be used in a semi-supervised setting to boost performance.
• Even with limited number of labeled images, the SSL GAN is able to perform better than the supervised baseline.
• The loss function used is of the form specified in the paper "Improved Techniques for Training GANs" https://arxiv.org/abs/1606.03498

Results for MNIST

No. of labeled samples per class	Accuracy - SSL GAN	Accuracy - Supervised
10	0.7220 ± 0.0247	0.6403 ± 0.0203
50	0.8985 ± 0.0609	0.8610 ± 0.0127
100	0.9325 ± 0.0269	0.9218 ± 0.0067
250	0.9693 ± 0.0149	0.9550 ± 0.0088
500	0.9760 ± 0.0065	0.9698 ± 0.0034
750	0.9818 ± 0.0038	0.9795 ± 0.0026
1000	0.9813 ± 0.0010	0.9830 ± 0.0012

Results for CIFAR10

No. of labeled samples per class	Accuracy - SSL GAN	Accuracy - Supervised
10	0.3430 ± 0.0552	0.1808 ± 0.0245
250	0.6070 ± 0.1061	0.4288 ± 0.0229
1000	0.7655 ± 0.0389	0.6500 ± 0.0358

Use Cases

• Radar Image analysis : A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images
• Medical Diagnostics : Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images
• In general, semi-supervised learning is useful when you have unlabeled samples (which cannot be made use of by supervised models) - usually when you cannot label large number of data - due to cost associated / unavailability of experts for the task.