LAUSR

We propose Improved SSGAN, a multi-Generator/Discriminator semi-supervised GAN architecture to address the well-known problem of mode collapse in addition to an improved classification for ordinal information. To reduce the vulnerability of the generator to a relatively superior discriminator, semi-supervised GAN was introduced to make the job of the discriminator tough. However, such architecture doesn’t solve the collapse problem where the generator is stuck generating some specific mode of the data. In this work, N-1 rank discriminators with two-dimensional outputs are proposed for ordinal information by applying rank estimation techniques. The first dimension in each discriminator is used to predict binary rank information which is aggregated to make the final prediction. The second dimension in each discriminator is independently used to train one or more generators where a collapse in any of the discriminators is supported by other discriminators. We have also extended the architecture to a conditioned generator where the output of one generator is fed into another, which improves image quality. Weight-sharing techniques among the discriminators have also shown a faster convergence during training. Through extensive experiments on age face data, we have demonstrated that Improved SSGAN outperforms the semi-supervised GAN both in image generation quality and age estimation.