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Adversarial Text to Continuous Image Generation

Kilichbek Haydarov · Aashiq Muhamed · Xiaoqian Shen · Jovana Lazarevic · Ivan Skorokhodov · Chamuditha Jayanga Galappaththige · Mohamed Elhoseiny

Arch 4A-E Poster #147
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper, we approach the text-to-image task from a different perspective, where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based GAN method on text inputs is not enough to achieve good performance. We propose a word-level attention-based weight modulation operator which controls the generation process of INR-GAN based on hypernetworks. Our experiments on benchmark datasets show that HyperCGAN achieves competitive performance to existing pixel-based methods and retain the properties of continuous generative models. Code, models, and benchmarks will be made publicly available.

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