Text-to-Image Generation with Attention Based Recurrent Neural Networks

• URL: http://arxiv.org/abs/2001.06658v1
• Date: Sat, 18 Jan 2020 12:19:19 GMT
• Title: Text-to-Image Generation with Attention Based Recurrent Neural Networks
• Authors: Tehseen Zia, Shahan Arif, Shakeeb Murtaza, and Mirza Ahsan Ullah
• Abstract summary: We develop a tractable and stable caption-based image generation model. Experimentations are performed on Microsoft datasets. Results show that the proposed model performs better than contemporary approaches.
• Score: 1.2599533416395765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conditional image modeling based on textual descriptions is a relatively new domain in unsupervised learning. Previous approaches use a latent variable model and generative adversarial networks. While the formers are approximated by using variational auto-encoders and rely on the intractable inference that can hamper their performance, the latter is unstable to train due to Nash equilibrium based objective function. We develop a tractable and stable caption-based image generation model. The model uses an attention-based encoder to learn word-to-pixel dependencies. A conditional autoregressive based decoder is used for learning pixel-to-pixel dependencies and generating images. Experimentations are performed on Microsoft COCO, and MNIST-with-captions datasets and performance is evaluated by using the Structural Similarity Index. Results show that the proposed model performs better than contemporary approaches and generate better quality images. Keywords: Generative image modeling, autoregressive image modeling, caption-based image generation, neural attention, recurrent neural networks.

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