# Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning
## 5 Discussion
### Qualitative Analysis  
The initial approaches involving the implementation of pseudo-labeling, and an addition of a classifier had the advantage of being simple to implement without additional heavy computational load. These techniques, however, were limited in performance, where other more complex techniques were seen to outperform this class of methods. Encoder-based techniques were introduced with the intention of leveraging the feature space in the training of the models. However, success of these techniques was dependent on the latent representations being representative of the classification task at hand and, therefore, an assumption that could vary based on the target domain. Additionally, the addition of an encoder resulted in increased computational requirements for the training, which might be limited based on the available hardware resources. Conditional approaches involved \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Citation** & **Category** & **Proposed Model** & **Results** \\ \hline
[30] & Pseudo-labeling and Classifiers & CatGAN & 1.91\% PI-MNIST test error w/100 labeled examples, outperforms all models except Ladder-full (1.13\%) \\ \hline
[39] & Encoder-based & ALI &
\begin{tabular}{c} CIFAR-10: 17.99 misclassification rate w/4000 labeled samples, \\ outperforms all; SVHN: 7.42 misclassification rate \\ w/1000 labeled samples, outperforms all \\ \end{tabular} \\ \hline
[26] & TripleGAN & TripleGAN &
\begin{tabular}{c} MNIST: 0.91\% error rate w/100 labeled samples, outperforms all except Conv-Ladder; SVHN: 5.77\% error rate w/1000 labeled samples, outperforms all except MMCVA; CIFAR-10: 16.99\% error rate \\ \end{tabular} \\ \hline
[44] & Manifold Regularization & SSVM-GAN &
\begin{tabular}{c} CIFAR-10: 14.27\% error rate w/4000 labels, outperformed all; SVHN: 4.54\% error rate w/1000 labels, outperformed all except Bad GAN \\ \end{tabular} \\ \hline
[28] & TripleGAN & R\({}^{3}\)-CGAN &
\begin{tabular}{c} SVHN: 2.79\% error rate w/1000 labels, outperformed all except equal \\ with EnhancedTGAN; CIFAR-10: 6.69\% error rate w/4000 labels, outperformed all \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Summary of notable works. discrimination in pairs of data points and labels with the classifier acting as a third player, which has been seen as a good solution to help resolve the conflict between having a good generator and a good classifier. However, the reliance on the class label as an input to the discriminator can be a point of failure in cases where the class distribution in the dataset is imbalanced to the extent that the discriminator only learns the majority classes to be real. In such a case, the generator will be strongly biased towards the majority class. A number of recent approaches have used manifold regularization to ensure that the model remains resistant to perturbations to input samples. However, such approaches rely on the assumption that any unseen data will lie on the same manifold as the perturbations used to perform the manifold regularization, which might fail in some cases based on the application domain. An interesting difference in approaches is also observed among the various techniques analyzed in terms of the training objectives. While one class of techniques focused on a strategy where the generator was weakened to boost the discriminator (e.g., GoodBadGAN [29]), a different class of techniques leveraged a good generator to boost the performance of the classifier (e.g., TripleGAN [26]). [PERSON] et al. [54] conducted a comparative analysis of these two techniques by training the GoodBadGAN as the BadGAN approach and the TripleGAN as the GoodGAN approach on the MNIST, CIFAR10, and SVHN benchmark datasets with a varying level of labeled samples. Their conclusion was that while GoodBadGAN outperformed TripleGAN when there were a medium number of labeled samples, TripleGAN performed better with less data, thus demonstrating a lack of sensitivity to the number of labeled samples.