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While attention-based transformer networks achieve unparalleled success innearly all language tasks, the large number of tokens (pixels) found in imagescoupled with the quadratic activation memory usage makes them prohibitive forproblems in computer vision. As such, while language-to-language translationhas been revolutionized by the transformer model, convolutional networks remainthe de facto solution for image-to-image translation. The recently proposedMLP-Mixer architecture alleviates some of the computational issues associatedwith attention-based networks while still retaining the long-range connectionsthat make transformer models desirable. Leveraging this memory-efficientalternative to self-attention, we propose a new exploratory model in unpairedimage-to-image translation called MixerGAN: a simpler MLP-based architecturethat considers long-distance relationships between pixels without the need forexpensive attention mechanisms. Quantitative and qualitative analysis showsthat MixerGAN achieves competitive results when compared to priorconvolutional-based methods.

MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation