# Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer
## 6 Conclusions  
In this work, in order to deal with imbalanced domains problem in the context of domain translation or domain transfer tasks, we have proposed a novel solution for overcoming the difficulties of the unsupervised learning process in such case. The proposed method combines the unsupervised CycleGAN architecture with SSL learning strategy that is additionally improved through an internal bootstrapping procedure (BTS). Thus, the BTS-SSL CycleGAN semi-supervised domain translation model was formed. Based on experimental results, it has exhibited potential to be a method of choice for the improvement of any CycleGAN-based domain translation task, especially in the considered imbalanced domains scenario. The proposed SSL strategy was used during the initial training stages to prevent discriminator related to the scarce domain from overfitting, while the employed BTS strategy for bootstrapping the statistics of the training pool of the discriminator was used to improve the learning performance in the unfavourable scenario of imbalanced domains. BTS was performed by inserting the example data generated by the corresponding internal generator, i.e., mapping from the fully observable into the scarce domain. We have manage to obtain significantly better results in comparison to the original CycleGAN method, and comparable with the fully supervised pix2 pix method, on several image domain transfer datasets. In further work, we will focus on other more intelligent ways to bootstrap the statistics of the discriminator of the scarce domain, by inserting into its pool only a certain amount of transferred image examples that \"fit\" the initial statistics of the mentioned pool sufficiently well. Conceptualization, M.J., L.K., B.P. and B.B. ; Data curation, B.P. ; Formal analysis, L.K. ; Funding acquisition, L.K. and B.P. ; Investigation, M.J. and B.B. ; Methodology, M.J., L.K. and B.P. ; Project administration, L.K. ; Software, B.P. ; Supervision, L.K. and B.P. ; Visualization L.K. ; Writing--original draft, M.J. and L.K. ; Writing--review & editing, B.B., L.K. and B.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the \"Science Fund of the Republic of Serbia\", through the project grant agreement No. 6524560: \"Speaker/Style Adaptation for Digital Voice Assistants Based on Image Processing Methods (AI-S-ADAPT); and by the \"Serbian Ministry of Education, Science and  
Figure 6: Visual examples of the proposed vs. baseline algorithm comparisons for 100% of the scarce domain data used: Real A (scarce domain) and Real B (target domain) correspond to image pair examples. Examples are given for _Google Maps_, _CityScapes_ and _Facade_ datasets. SSL denotes that only SSL on the control part of pared images is included in the training of CycleGAN, BTS denotes that only bootstrapping of the statistic of the A discriminator pool is included in the training, while SSL+BTS denotes that both SSL and BTS are included. Technological Development\" through the research project No. 451-03-68/2020-14/200156: \"Innovative Scientific and Artistic Research from the Faculty of Technical Sciences Activity Domain\". Not applicable. **Data Availability Statement:** All data sources are publicly available and mentioned in the references. This research was supported by the \"Science Fund of the Republic of Serbia\", through the project grant agreement No. 6524560: \"Speaker/Style Adaptation for Digital Voice Assistants Based on Image Processing Methods (AI-S-ADAPT), and by the \"Serbian Ministry of Education, Science and Technological Development\" through the research project No. 451-03-68/2020-14/200156: \"Innovative Scientific and Artistic Research from the Faculty of Technical Sciences Activity Domain\". The authors would also like to acknowledge support by the EU's H2020 research and innovation programme under the project grant agreement No.