Deep Generative Multimedia Children's Literature
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Abstract (arXiv)
Artistic work leveraging Machine Learning techniques is an increasingly popular endeavour for those with a creative lean. However, most work is done in a single domain: text, images, music, etc. In this work, I design a system for a machine learning created multimedia experience, specifically in the genre of children's literature. We detail the process for exclusively using publicly available pretrained deep neural network based models, I present multiple examples of the work my system creates, and I explore the problems associated in this area of creative work.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 17 references that are in the map. First posted on arXiv 27 Sep 2022.
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12 of 17 references.
References · 12
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2017Neural Discrete Representation Learningcited by 4,650
- 2021Zero-Shot Text-to-Image Generationcited by 4,276
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
- 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 555
- 2020Jukebox: A Generative Model for Musiccited by 535
- 2021RedCaps: web-curated image-text data created by the people, for the peoplecited by 175
- 20193D Ken Burns Effect from a Single Imagecited by 128
- 2022General-purpose, long-context autoregressive modeling with Perceiver ARcited by 60
- 2018MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separationcited by 19
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