Jul 12, 2020
Recent work has shown GANs can generate highly realistic images that are indistinguishable by human. Of particular interest here is the empirical observation that most generated images are not contained in training datasets, indicating potential generalization with GANs. That generalizability makes it appealing to exploit GANs to help applications with limited available data, for example to augment training data to alleviate overfitting. To better facilitate training a GAN on limited data, we propose to leverage already-available GAN models pretrained on large-scale datasets (like ImageNet) to introduce additional common knowledge (which may not exist within the limited data) following the transfer learning idea. Specifically, exampled by natural image generation tasks, we reveal the fact that low-level filters (those close to observations) of both the generator and discriminator of pretrained GANs can be transferred to help the target limited-data generation. For better adaption of the transferred filters to the target domain, we introduce a new technique named adaptive filter modulation (AdaFM), which provides boosted performance over baseline methods. Unifying the transferred filters and the introduced techniques, we present our method and conduct extensive experiments to demonstrate its training efficiency and better performance on limited-data generation.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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