Research Area: Machine Learning
Convolutional neural networks (CNNs) have shown dramatic improvements in single image super-resolution (SISR) by using large-scale external samples. Despite their remarkable performance based on the external dataset, they cannot exploit internal information within a specific image. Another problem is that they are applicable only to the specific condition of data that they are supervised. For instance, the low-resolution (LR) image should be a "bicubic" downsampled noise-free image from a high-resolution (HR) one. To address both issues, zero-shot super-resolution (ZSSR) has been proposed for flexible internal learning. However, they require thousands of gradient updates, i.e., long inference time. In this paper, we present Meta-Transfer Learning for Zero-Shot Super-Resolution (MZSR), which leverages ZSSR. Precisely, it is based on finding a generic initial parameter that is suitable for internal learning. Thus, we can exploit both external and internal information, where one single gradient update can yield quite considerable results. With our method, the network can quickly adapt to a given image condition. In this respect, our method can be applied to a large spectrum of image conditions within a fast adaptation process.
Keywords:
Pattern Recognition
Computer Vision
Meta-Transfer Learning
Convolutional neural networks
Zero-shot super-resolution
Author(s) Name: Jae Woong Soh, Sunwoo Cho, Nam Ik Cho
Journal name:
Conferrence name: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher name: IEEE/CVF
DOI:
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