# Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer
###### Abstract  
Most CycleGAN domain transfer architectures require a large amount of data belonging to domains on which the domain transfer task is to be applied. Nevertheless, in many real-world applications one of the domains is reduced, i.e., scarce. This means that it has much less training data available in comparison to the other domain, which is fully observable. In order to tackle the problem of using CycleGAN framework in such unfavorable application scenarios, we propose and invoke a novel Bootstrapped SSL CycleGAN architecture (BTS-SSL), where the mentioned problem is overcome using two strategies. Firstly, by using a relatively small percentage of available labelled training data from the reduced or scarce domain and a Semi-Supervised Learning (SSL) approach, we prevent overfitting of the discriminator belonging to the reduced domain, which would otherwise occur during initial training iterations due to the small amount of available training data in the scarce domain. Secondly, after initial learning guided by the described SSL strategy, additional bootstrapping (BTS) of the reduced data domain is performed by inserting artifically generated training examples into the training poll of the data discriminator belonging to the scarce domain. Bootstrapped samples are generated by the already trained neural network that performs transferring from the fully observable to the scarce domain. The described procedure is periodically repeated during the training process several times and results in significantly improved performance of the final model in comparison to the original unsupervised CycleGAN approach. The same also holds in comparison to the solutions that are exclusively based either on the described SSL, or on the bootstrapping strategy, i.e., when these are applied separately. Moreover, in the considered scarce scenarios it also shows competitive results in comparison to the fully supervised solution based on the pix2 pix method. In that sense, it is directly applicable to many domain transfer tasks that are relying on the CycleGAN architecture. CycleGAN architecture; semi-supervised learning; bootstrapping; imbalanced data Article  
Domain Transfer  
[PERSON], L.  
images are assumed to be available in pairs corresponding to the samples denoting the same thing in the source and the target domain (i.e., in the \"first\" and the \"second\" domain). [PERSON] et al. [13] have first applied cGAN in the domain translation task, by proposing supervised pix2 pix learning strategy. This approach was further developed by [14; 15; 16], but those solutions still failed to capture the complex structural relationships of the scenes in the cases when, e.g., two domains have drastically different views, i.e., while trying to achieve mapping through a single translation network. Nevertheless, despite the efforts to improve the supervised methods, for many domain transfer tasks paired training images are not fully available. Therefore, besides the described approaches, almost at the same time an unsupervised domain transfer method known as CycleGAN was proposed in [17]. It utilizes two GANs oriented in opposite directions, i.e., from one domain into another, and vice versa. As introduced mappings that are learned are highly under-constrained, cycle consistency loss was introduced in [17] to reduce the mentioned anomaly. Cycle consistency constraint proved to be very effective as a technique for preserving semantic information of the data with respect to a task of domain transfer, and is therefore applied in various tasks, e.g., in image-to-image translation [17], emotion style transfer [18], speech enhancement [19], etc. However, many of these problems, such as emotion style transfer, speech enhancement, speaker domain transfer, medical image domain transfer, have inherent domain asymmetry, in the sense that one of the domains has significantly less training data available. Therefore, such domain is usually named as reduced or _scarce_ in comparison to another one, which is considered as fully observable.