Minegan: effective knowledge transfer from gans to target domains with few images
One of the attractive characteristics of deep neural networks is their ability to transfer
knowledge obtained in one domain to other related domains. As a result, high-quality
networks can be trained in domains with relatively little training data. This property has been
extensively studied for discriminative networks but has received significantly less attention
for generative models. Given the often enormous effort required to train GANs, both
computationally as well as in the dataset collection, the re-use of pretrained GANs is a …
knowledge obtained in one domain to other related domains. As a result, high-quality
networks can be trained in domains with relatively little training data. This property has been
extensively studied for discriminative networks but has received significantly less attention
for generative models. Given the often enormous effort required to train GANs, both
computationally as well as in the dataset collection, the re-use of pretrained GANs is a …
[PDF][PDF] MineGAN: effective knowledge transfer from GANs to target domains with few images
One of the attractive characteristics of deep neural networks is their ability to transfer
knowledge obtained in one domain to other related domains. As a result, high-quality
networks can be trained in domains with relatively little training data. This property has been
extensively studied for discriminative networks but has received significantly less attention
for generative models. Given the often enormous effort required to train GANs, both
computationally as well as in the dataset collection, the re-use of pretrained GANs is a …
knowledge obtained in one domain to other related domains. As a result, high-quality
networks can be trained in domains with relatively little training data. This property has been
extensively studied for discriminative networks but has received significantly less attention
for generative models. Given the often enormous effort required to train GANs, both
computationally as well as in the dataset collection, the re-use of pretrained GANs is a …
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