# Survey on Implementations of Generative Adversarial Networks for Semi-Supervised Learning
## 4 Results  
Tables 2-6 show lists of the sources reviewed and the techniques they set as the baseline comparison to their results. In order to provide ease of analysis, the different works reported are grouped as per the architecture followed by the work as discussed in the framework section. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Citation** & **Authors** & **Date of Publication** & **Proposed Model** & **Baseline Models** \\ \hline
[49] & [PERSON] et al. & June 2014 & GAN (Original) & n/a \\ \hline
[30] & [PERSON] & April 2016 & CatGAN (Categorical) & MTC, PEA, PEA+, VAE + SVM, SS-VAE, Ladder \\  & & & & T-model, Ladder-full \\ \hline
[32] & [PERSON] et al. & June 2016 & Improved GAN & DGN, Virtual Adversarial, CatGAN, Skip Keep Generative Model, Ladder network, Auxiliary Deep Generative Model \\ \hline
[31] & [PERSON] & October 2016 & SGAN (Semi-Supervised) & CNN (isolated classifier, unspecified) \\ \hline
[29] & [PERSON] et al. & November 2017 & GoodBadGAN & CatGAN, SDGM, Ladder network, ADGM, FM, ALI, VAT small, TripleGAN, II model, VAT + EntMin + Large \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Baseline Models of Pseudo-labeling and Classifier approaches. Figure 7: SS-GAN architecture. \begin{table}
\begin{tabular}{c c c c c} \hline
**Citation** & **Authors** & **Date of Publication** & **Proposed Model** & **Baseline Models** \\ \hline
[26] & [PERSON] et al. & November 2017 & TripleGAN & M1 + M2, VAT, Ladder, Conv-Ladder, ADGM, SDGM, MMCVA, CatGAN, Improved GAN, ALI \\ \hline
[35] & [PERSON] et al. & November 2017 & TriangleGAN & CatGAN, Improved GAN, ALI, TripleGAN \\ \hline
[36] & [PERSON] et al. & November 2017 & SGAN (Structured) & Ladder, VAE, CatGAN, ALI, Improved GAN, TripleGAN \\ \hline
[27] & [PERSON] and [PERSON] & November 2019 & MarginGAN & NN, SVM, CNN, TSVM, DBN+NCA, EmbedNN, CAE, MTC \\ \hline
[34] & [PERSON] et al. & January 2020 & EnhancedTGAN (Triple) & Ladder network, SFCTN, TI model, Temporal Ensembling, Mean Teacher, VAT, VAdD, VAHD + VAT, SNTG + TI model, SNTG + VAT, CatGAN, Improved GAN, ALI, TripleGAN, GoodBadGAN, CT-GAN, TripleGAN \\ \hline
[28] & [PERSON] et al. & August 2020 & R\({}^{3}\)-CGAN (Random Regional Replacement Class-Conditional) & LSTG + TI model, Deep Co-Train, CCN, ICT, CatGAN, Improved GAN, ALI, TripleGAN, Triangle-GAN, GoodBadGAN, CT-GAN, EnhancedTGAN \\ \hline
[43] & [PERSON] & March 2021 & EC-GAN (External Classifier) & DCGAN \\ \hline \end{tabular}
\end{table} 
Table 4: Baseline Models for TripleGAN implementations. \begin{table}
\begin{tabular}{c c c c} \hline
**Citation** & **Authors** & **Date of Publication** & **Proposed Model** & **Baseline Models** \\ \hline
[19] & [PERSON] et al. & March 2018 & CT-GAN & Ladder, VAT, CatGAN, Improved GAN, TripleGAN \\ \hline
[33] & [PERSON] et al. & October 2020 & MatchGAN & StarGAN \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline
**Citation** & **Authors** & **Date of Publication** & **Proposed Model** & **Baseline Models** \\ \hline
[19] & [PERSON] et al. & March 2018 & CT-GAN & Ladder, VAT, CatGAN, Improved GAN, TripleGAN \\ \hline
[33] & [PERSON] et al. & October 2020 & MatchGAN & StarGAN \\ \hline \end{tabular}
\end{table} 
Table 3: Baseline Models of Encoder-based Approaches. \begin{table}
\begin{tabular}{c c c c} \hline
**Citation** & **Authors** & **Date of Publication** & **Proposed Model** & **Baseline Models** \\ \hline
[38] & [PERSON] et al. & May 2016 & BiGAN & - \\ \hline
[39] & [PERSON] et al. & February 2017 & ALI (Adversarially Learned Inference) & CIFAR-10: Ladder network, CatGAN, GAN \\  & & February 2017 & Learned Inference) & ([PERSON] 2016); SVHN: VAE, SWWAE, DCGAN + L2 SVM, SDGM, GAN ([PERSON] 2016) \\ \hline
[40] & [PERSON] et al. & December 2017 & Augmented BiGAN \\ \hline \end{tabular}
\end{table} 
Table 3: Baseline Models of Encoder-based Approaches.