xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network
This work addresses a specific bottleneck in generative models for researchers and practitioners in computer vision, though it is incremental as it builds on existing CycleGAN and explainability methods.
The paper tackles the slow convergence of CycleGAN in unsupervised image-to-image transformation by using discriminator-driven explainability with saliency maps to mask gradients and adding noise-based counterfactual filtering, resulting in a much higher convergence rate while preserving image quality.
In the domain of unsupervised image-to-image transformation using generative transformative models, CycleGAN has become the architecture of choice. One of the primary downsides of this architecture is its relatively slow rate of convergence. In this work, we use discriminator-driven explainability to speed up the convergence rate of the generative model by using saliency maps from the discriminator that mask the gradients of the generator during backpropagation, based on the work of Nagisetty et al., and also introducing the saliency map on input, added onto a Gaussian noise mask, by using an interpretable latent variable based on Wang M.'s Mask CycleGAN. This allows for an explainability fusion in both directions, and utilizing the noise-added saliency map on input as evidence-based counterfactual filtering. This new architecture has much higher rate of convergence than a baseline CycleGAN architecture while preserving the image quality.