Sep 3, 2022 · We thereby propose a novel Dynamical Semantic Evolution GAN (DSE-GAN) to re-compose each stage's text features under a novel single adversarial multi-stage architecture.

Oct 10, 2022 · We propose a novel sequential generation framework on both text and images for T2I, i.e., DSE-GAN, which dynamically re-composes text features based on the historical stage. To the best of our knowledge, this is the first framework in T2I that adaptively re-composes text features at each stage.

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ISEGAN (Iterated SEGAN) and DSEGAN (Deep SEGAN) were built upon the SEGAN proposed by Pascual et al. and SEGAN repository from santi-pdp. Different from SEGAN with a single generator, ISEGAN and DSEGAN have multiple generators which are chained to perform multi-stage enhancement mapping:

Sep 3, 2022 · Download Citation | DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation | Text-to-image generation aims at generating realistic images which are ...

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exibly learn different enhancement mappings at different stages of the net-work at the cost of an increased model size. We demonstrate that the proposed multi-stage enhancement approach outperforms. the one-stage SEGAN baseline, where the independent generators lead.

May 28, 2022 · In this paper, we present super-resolution generative adversarial network (SRGAN). To our knowledge, it is the first framework capable of recovering photo-realistic natural images from 4 times...

dsegan has 3 repositories available. Follow their code on GitHub.

May 27, 2022 · DSegAN: A Deep Light-weight Segmentation-based Attention Network for Image Restoration Abstract: Feature attention is a technique used in deep neural networks to provide a discriminative processing of the various regions in an image based on their significance for enhancing the image restoration performance.