Abstract:
Modern developments in the fields of natural language processing (NLP)
and computer vision (CV) emphasize the increasing importance of
generating images from text descriptions. The presented article analyzes and
compares two key methods in this area: generative adversarial network with
conditional latent semantic analysis (GAN-CLS) and ultra-long transformer
network (XLNet). The main components of GAN-CLS, including the
generator, discriminator, and text encoder, are discussed in the context of
their functional tasks—generating images from text inputs, assessing the
realism of generated images, and converting text descriptions into latent
spaces, respectively. A detailed comparative analysis of the performance of
GAN-CLS and XLNet, the latter of which is widely used in the organic
light-emitting diode (OEL) field, is carried out. The purpose of the study is
to determine the effectiveness of each method in different scenarios and then
provide valuable recommendations for selecting the best method for
generating images from text descriptions, taking into account specific tasks
and resources. Ultimately, our paper aims to be a valuable research resource
by providing scientific guidance for NLP and CV experts.
