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Poster
Few-shot Image Generation with Elastic Weight Consolidation
Yijun Li · Richard Zhang · Jingwan (Cynthia) Lu · Eli Shechtman

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #927

Few-shot image generation seeks to generate more data of a given domain, with only few available training examples. As it is unreasonable to expect to fully infer the distribution from just a few observations (e.g., emojis), we seek to leverage a large, related source domain as pretraining (e.g., human faces). Thus, we wish to preserve the diversity of the source domain, while adapting to the appearance of the target. We adapt a pretrained model, without introducing any additional parameters, to the few examples of the target domain. Crucially, we regularize the changes of the weights during this adaptation, in order to best preserve the information of the source dataset, while fitting the target. We demonstrate the effectiveness of our algorithm by generating high-quality results of different target domains, including those with extremely few examples (e.g., 10). We also analyze the performance of our method with respect to some important factors, such as the number of examples and the similarity between the source and target domain.

Author Information

Yijun Li (Adobe Research)
Richard Zhang (Adobe)

Richard Zhang is a Research Scientist at Adobe Research, with interests in computer vision, deep learning, machine learning, and graphics. He obtained his PhD in EECS, advised by Professor Alexei A. Efros, at UC Berkeley in 2018. He graduated summa cum laude with BS and MEng degrees from Cornell University in ECE. He is a recipient of the 2017 Adobe Research Fellowship. More information can be found on his webpage: http://richzhang.github.io/.

Jingwan (Cynthia) Lu (Adobe Research)

Jingwan has a passion for data-driven content creation. Her primary research focus is to apply deep generative models for photography applications. Her vision is to harness the power of machine learning in the age of data explosion to invent the next generation image and video editing tools. She also worked on brush models, stylization, guided texture synthesis, voice synthesis, etc. using various data-driven approaches.

Eli Shechtman (Adobe Research, US)

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