# Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer
###### Abstract
According to the authors' knowledge, only several approaches tackled the mentioned problem in the literature, i.e., analyzed domain transfer in cases when one of the two domains is scarce. In [20], an augmented cyclic adversarial learning model that enforces the cycle-consistency constraint via an external task-specific model is proposed. Additionally, in [21], the authors add semi-supervised task-specific attention modules to generate images that are used for improving the performance in a medical image classification task. However, in all those papers, a task-specific approach is used in forming additional cost functions, which means that the analysis of each specific task is required for problem formulation, as well during the training procedure. In this work, we propose a different approach. We combine the original unsupervised CycleGAN architecture with a semi-supervised learning (SSL) strategy, as well as with a bootstrapping (BTS) procedure. SSL is used to prevent overfitting of the CycleGAN discriminator that is belonging (or being related) to the assumed scarce domain in the considered domain transfer mapping. Discriminator overfitting could occur during the initial learning iterations of the unsupervised CycleGAN due to insufficient number of training samples in the scarce domain, and consequently lead to an underperforming GAN. On the other hand, the second part of the proposed method, i.e., the bootstrapping strategy (BTS), has the aim to bootstrapp the statistics of the problematic discriminator belonging to the scarce domain (i.e., its training pool statistics). This is accomplished by inserting (bootstrapping) artificially generated example data into the discriminator's training pool from time to time during the training procedure. Namely, the corresponding generator residing in the second, fully observable domain of the CycleGAN is periodically called during the training to randomly produce the necessary samples in the scarce domain, and thus compensate the fact that the original training pool of the discriminator in the scarce domain was inadequate. This sampling procedure is applied periodically during the training, but for the first time only after the necessary generator is already sufficiently well trained, i.e., after the initial SSL based on a small amount of paired samples from both domains is performed. Elements of the described BTS-SSL method, i.e., combined SSL and BTS procedure (\"SSL+BTS\") that is applied during the proposed SSL of CycleGAN in the considered asymmetric domain size scenarios, are illustrated in Figure 1.  
The initial SSL strategy that is based on a small set of paired observations allows discriminator as well the generator that are residing in the scarce domain to avoid overfittingand learn necessary parameters to some extent. In the latter stages of the training procedure the learning process is further guided by the combined SSL as well as by the usual adversarial training strategy, but with an additional augmentation of the discriminator's training pool in the scarce domain, from time to time. This is illustrated in the right part of Figure 1, where it is highlighted that the training involves three types of data, as compared to only one in the baseline unsupervised case. Actually, during the training, after a number of initial iterations, the pool corresponding to the discriminator of the scarce domain is constantly (periodically) filed by training examples generated by the network \(F\) in Figure 1, which is transferring the training examples from the full to the scarce domain. Thus, contrary to the approach proposed in [20], where the domain attention mechanism is utilized, the proposed BTS-SSL model is independent of the specific domain transfer task, and requires only a relatively small amount of paired examples in the very beginning of the training procedure. This property makes it directly applicable to different tasks. The paper is organized as follows.