Text-to-Image Generation with Attention Based Recurrent Neural Networks
This work addresses the problem of generating high-quality images from text for applications in unsupervised learning, though it appears incremental by building on existing methods like attention and autoregressive models.
The paper tackles conditional image generation from text descriptions by proposing a tractable and stable model using attention-based RNNs, which outperforms contemporary approaches on datasets like Microsoft COCO and MNIST-with-captions, as measured by the Structural Similarity Index.
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.