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To facilitate the real-world applications of text-to-image synthesis, we focus on studying the following three issues: (1) How to ensure that generated samples are believable, realistic or natural? (2) How to exploit the latent space of the generator to edit a synthesized image? (3) How to improve the explainability of a text-to-image generation framework? We introduce two new data sets for benchmarking, i.e., the <jats:italic>Good <\/jats:italic> &amp; <jats:italic>Bad<\/jats:italic>, bird and face, data sets consisting of successful as well as unsuccessful generated samples. This data set can be used to effectively and efficiently acquire high-quality images by increasing the probability of generating <jats:italic>Good<\/jats:italic> latent codes with a separate, new classifier. Additionally, we present a novel algorithm which identifies semantically understandable directions in the latent space of a conditional text-to-image GAN architecture by performing independent component analysis on the pre-trained weight values of the generator. Furthermore, we develop a background-flattening loss (BFL), to improve the background appearance in the generated images. Subsequently, we introduce linear-interpolation analysis between pairs of text keywords. This is extended into a similar triangular \u2018linguistic\u2019 interpolation. The visual array of interpolation results gives users a deep look into what the text-to-image synthesis model has learned within the linguistic embeddings. Experimental results on the recent DiverGAN generator, pre-trained on three common benchmark data sets demonstrate that our classifier achieves a better than 98% accuracy in predicting Good\/Bad classes for synthetic samples and our proposed approach is able to derive various interpretable semantic properties for the text-to-image GAN model.<\/jats:p>","DOI":"10.1007\/s00521-023-09185-6","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T20:02:50Z","timestamp":1700596970000},"page":"2549-2572","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Optimizing and interpreting the latent space of the conditional text-to-image GANs"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2887-9873","authenticated-orcid":false,"given":"Zhenxing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lambert","family":"Schomaker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"9185_CR1","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784"},{"key":"9185_CR2","doi-asserted-by":"crossref","unstructured":"Zhang Z, Schomaker L (2022) Optgan: Optimizing and interpreting the latent space of the conditional text-to-image gans. arXiv preprint arXiv:2202.12929","DOI":"10.1007\/s00521-023-09185-6"},{"key":"9185_CR3","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.neucom.2021.12.005","volume":"473","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Schomaker L (2021) Divergan: an efficient and effective single-stage framework for diverse text-to-image generation. 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