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Authors: Rajiv Kumar and G. Sivakumar

Affiliation: Department of CSE, IIT Bombay, Mumbai, India

Keyword(s): One-shot Learning, Few-shot Learning, Generative Modelling, Adversarial Learning, Data Efficient GAN.

Abstract: Requirements of large amounts of data is a difficulty in training many GANs. Data efficient GANs involve fitting a generator’s continuous target distribution with a limited discrete set of data samples, which is a difficult task. Single image methods have focused on modelling the internal distribution of a single image and generating its samples. While single image methods can synthesize image samples with diversity, they do not model multiple images or capture the inherent relationship possible between two images. Given only a handful number of images, we are interested in generating samples and exploiting the commonalities in the input images. In this work, we extend the single-image GAN method to model multiple images for sample synthesis. We modify the discriminator with an auxiliary classifier branch, which helps to generate wide variety of samples and to classify the input labels. Our Data-Efficient GAN (DEff-GAN) generates excellent results when similarities and correspondence s can be drawn between the input images/classes. (More)

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Paper citation in several formats:
Kumar, R. and Sivakumar, G. (2023). DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 870-877. DOI: 10.5220/0011799600003417

@conference{visapp23,
author={Rajiv Kumar and G. Sivakumar},
title={DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={870-877},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011799600003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis
SN - 978-989-758-634-7
IS - 2184-4321
AU - Kumar, R.
AU - Sivakumar, G.
PY - 2023
SP - 870
EP - 877
DO - 10.5220/0011799600003417
PB - SciTePress

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