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arXiv · 1906.11888

Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks

Seungjoo Yoo·Hyojin Bahng·Sunghyo Chung·Junsoo Lee·Jaehyuk Chang·Jaegul Choo
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Abstract

Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, we present a novel memory-augmented colorization model MemoPainter that can produce high-quality colorization with limited data. In particular, our model is able to capture rare instances and successfully colorize them. We also propose a novel threshold triplet loss that enables unsupervised training of memory networks without the need of class labels. Experiments show that our model has superior quality in both few-shot and one-shot colorization tasks.

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Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung, Junsoo Lee, Jaehyuk Chang, Jaegul Choo. 2019-06-09. Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks. https://arxiv.org/abs/1906.11888

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