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In the domain of unsupervised image-to-image transformation using generativetransformative models, CycleGAN has become the architecture of choice. One ofthe primary downsides of this architecture is its relatively slow rate ofconvergence. In this work, we use discriminator-driven explainability to speedup the convergence rate of the generative model by using saliency maps from thediscriminator that mask the gradients of the generator during backpropagation,based on the work of Nagisetty et al., and also introducing the saliency map oninput, added onto a Gaussian noise mask, by using an interpretable latentvariable based on Wang M.'s Mask CycleGAN. This allows for an explainabilityfusion in both directions, and utilizing the noise-added saliency map on inputas evidence-based counterfactual filtering. This new architecture has muchhigher rate of convergence than a baseline CycleGAN architecture whilepreserving the image quality.

xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network