Examining performance of sketch-to-image translation models with multiclass automatically generated paired training data

Dichao Hu

doi:10.1117/12.2521309

22 March 2019 Examining performance of sketch-to-image translation models with multiclass automatically generated paired training data

Dichao Hu

Author Affiliations +

Proceedings Volume 11049, International Workshop on Advanced Image Technology (IWAIT) 2019; 110490F (2019) https://doi.org/10.1117/12.2521309
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

Image translation is a computer vision task that involves translating one representation of the scene into another. Various approaches have been proposed and achieved highly desirable results. Nevertheless, its accomplishment requires abundant paired training data which are expensive to acquire. Therefore, models for translation are usually trained on a set of paired training data which are carefully and laboriously designed. Our work is focused on learning through automatically generated paired data. We propose a method to generate fake sketches from images using an adversarial network and then pair the images with corresponding fake sketches to form large-scale multi-class paired training data for training a sketch-to-image translation model. Our model is an encoder-decoder architecture where the encoder generates fake sketches from images and the decoder performs sketch-to-image translation. Qualitative results show that the encoder can be used for generating large-scale multi-class paired data under low supervision. Our current dataset now contains 61255 image and (fake) sketch pairs from 256 different categories. These figures can be greatly increased in the future thanks to our weak reliance on manually labelled data.

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Dichao Hu "Examining performance of sketch-to-image translation models with multiclass automatically generated paired training data", Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490F (22 March 2019); https://doi.org/10.1117/12.2521309

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KEYWORDS

Computer programming

Data modeling

Performance modeling

Data acquisition

Network architectures

Visual process modeling

Computer vision technology

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