Browsing by Author "Lin, Yikang"

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    Inductive Biases in Generative Adversarial Networks
    (2023) Lin, Yikang; Grissom, Alvin
    In unsupervised learning, generative adversarial network (GAN) is one the generative models for generating new examples that are not present in the original data. Although GANs have impressive results in generating novel examples, the inductive biases of the model can lead to biased results in image generation. This paper provides a summary of two types of GAN-the MLP GAN and StyleGAN-and their corresponding inductive biases. Because analytical analysis of the effects of GAN inductive biases is difficult, an empirical method for studying the inductive biases is also discussed.