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Abstract: In recent years, an increasing trend towards GaN integration can be observed, enabled by the lateral structure of the GaN technology. A key improvement over a discrete implementation is the ...
Abstract: Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited memory of ...
Our method MI-GAN can produce plausible results both on complex scene images as well as on face images. The bubble chart on the right shows the advantage of our network over state-of-the-art ...
COT-GAN is an adversarial algorithm to train implicit generative models optimized for producing sequential data. The loss function of this algorithm is formulated using ideas from Causal Optimal ...
The advent of large-scale training has produced a cornucopia of powerful visual recognition models. However, generative models, such as GANs, have traditionally been trained from scratch in an ...
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