# Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning
## 5 Discussion  
### Quantitative Analysis  
A number of GANs were chosen as representative models from each technique. CatGAN was chosen from the initial implementations. Similarly, ALI was the model of choice for comparison among the encoder-based architectures, and TripleGAN was used as the baseline of choice for its class of models. Table 12 displays a summary of notable works across categories and their results in order to enable a deeper comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Citation** & **Proposed Model** & **Datasets Evaluated On** & **Results** \\ \hline \multirow{4}{*}{[41]} & \multirow{4}{*}{Laplacian-based GAN} & \multirow{4}{*}{SVHN, CIFAR-10} & SVHN: 4.51\% error rate w/1000 labeled, outperformed all except \\  & & & Vat + EntMin, Improved semi-GAN, and Bad GAN; CIFAR-10: 14.45\% \\  & & & error rate w/4000 labeled, outperformed all except Vat + EntMin and \\  & & & Bad GAN \\ \hline \multirow{4}{*}{[42]} & \multirow{4}{*}{Monte Carlo-based GAN} & \multirow{4}{*}{CIFAR-10, SVHN} & CIFAR-10: 14.34\% error rate w/4000 labels, outperformed all except \\  & & & VAT, VAT + EntMin, and Local GAN; SVHN: 4.63\% error rate \\  & & & \(w/1000 labels, outperformed VAT + EntMin and Improved semi-GAN \\ \hline \multirow{4}{*}{[43]} & \multirow{4}{*}{SelfAttentionGAN} & \multirow{4}{*}{SVHN, CIFAR-10} & CIFAR-10: 9.87\% error rate w/4000 labels, outperformed all; SVHN: 4.30\% error rate w/1000 labels, outperformed all except Bad GAN, \\  & & & VAT + EntMin w/aug, MeanTeacher w/aug, VAT + Ent + SNGT w/aug \\ \hline \multirow{4}{*}{[44]} & \multirow{4}{*}{SSVM-GAN (Scalable SVM)} & \multirow{4}{*}{CIFAR-10, SVHN} & CIFAR-10: 14.27\% error rate w/4000 labels, outperformed all; SVHN: 4.54\% error rate w/1000 labels, outperformed all except Bad GAN \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Manifold Regularization-based approaches Results Summary. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Citation** & **Proposed Model** & **Datasets Evaluated On** & **Results** \\ \hline \multirow{4}{*}{[48]} & \multirow{4}{*}{SS-GAN (Semi-Supervised)} & \multirow{4}{*}{MNIST, CelebA, CIFAR-10} & MNIST: 0.1044 class prediction error, outperforms only SA-GAN, 0.0160 reconstruction error, outperforms SA-GAN and SC-GAN (both metrics w/20 labeled samples); CelebA: 0.040 reconstruction error, outperforms all except C-GAN; CIFAR-10: 0.299 class pred error, outperforms only AC-GAN and SC-GAN, 0.061 error error, outperforms all except C-GAN \\ \hline \multirow{4}{*}{[47]} & \multirow{4}{*}{\
\begin{tabular}{c} IAGAN \\ (Inception-Augmentation) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Pneumonia X-rays: Dataset \\ (3765 imgs), Dataset II \\ (4700 imgs) \\ \end{tabular} } & \multirow{4}{*}{Dataset I: 0.90 AUC, outperformed all; Dataset II: 0.76 AUC, outperformed all} \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{2}{*}{[45]} & \multirow{2}{*}{MCGAN (Multi-Class)} & \multirow{2}{*}{MNIST, F-MNIST} & MNIST: 0.9 AUC unknown class classification and 0.84 known class \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\  & & & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Assorted Approaches Results Summary.