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Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>Training generative adversarial networks (GANs) for noise-to-image synthesis is a challenge task, primarily due to the instability of GANs\u2019 training process. One of the key issues is the generator\u2019s sensitivity to input data, which can cause sudden fluctuations in the generator\u2019s loss value with certain inputs. This sensitivity suggests an inadequate ability to resist disturbances in the generator, causing the discriminator\u2019s loss value to oscillate and negatively impacting the discriminator. Then, the negative feedback of discriminator is also not conducive to updating generator\u2019s parameters, leading to suboptimal image generation quality. In response to this challenge, we present an innovative GANs model equipped with a learnable auxiliary module that processes auxiliary noise. The core objective of this module is to enhance the stability of both the generator and discriminator throughout the training process. To achieve this target, we incorporate a learnable auxiliary penalty and an augmented discriminator, designed to control the generator and reinforce the discriminator\u2019s stability, respectively. We further apply our method to the Hinge and LSGANs loss functions, illustrating its efficacy in reducing the instability of both the generator and the discriminator. The tests we conducted on LSUN, CelebA, Market-1501, and Creative Senz3D datasets serve as proof of our method\u2019s ability to improve the training stability and overall performance of the baseline methods.<\/jats:p>","DOI":"10.1145\/3653021","type":"journal-article","created":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T07:44:05Z","timestamp":1710661445000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Generative Adversarial Networks with Learnable Auxiliary Module for Image Synthesis"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6716-0039","authenticated-orcid":false,"given":"Yan","family":"Gan","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China and Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6376-2980","authenticated-orcid":false,"given":"Chenxue","family":"Yang","sequence":"additional","affiliation":[{"name":"Agricultural Information Institute of CAAS, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4760-8702","authenticated-orcid":false,"given":"Mao","family":"Ye","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9996-3354","authenticated-orcid":false,"given":"Renjie","family":"Huang","sequence":"additional","affiliation":[{"name":"Southwest University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2259-886X","authenticated-orcid":false,"given":"Deqiang","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"issue":"5","key":"e_1_3_2_2_2","first-page":"169","article-title":"Coefficient of variation","volume":"1","author":"Abdi Herv\u00e9","year":"2010","unstructured":"Herv\u00e9 Abdi. 2010. 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