Creativity and Machine Learning: A Survey
arXiv:2104.02726 · doi:10.1145/3664595
Abstract
There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, key machine learning techniques (including generative deep learning), and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current research challenges and emerging opportunities in this field.
Published in ACM Computing Surveys at https://dl.acm.org/doi/10.1145/3664595
References in corpus (22)
- Sequence to Sequence Learning with Neural Networks
- The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
- Cascaded Diffusion Models for High Fidelity Image Generation
- Towards artificial general intelligence via a multimodal foundation model
- A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
- GANSynth: Adversarial Neural Audio Synthesis
- CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
- Long Text Generation via Adversarial Training with Leaked Information
- Diffusion Models: A Comprehensive Survey of Methods and Applications
- Adversarial Feature Matching for Text Generation
- Jukebox: A Generative Model for Music
- A survey of multimodal deep generative models
- Copyright in Generative Deep Learning
- Counterpoint by Convolution
- The Lovelace 2.0 Test of Artificial Creativity and Intelligence
- Transforming Exploratory Creativity with DeLeNoX
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
- FUDGE: Controlled Text Generation With Future Discriminators
- Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges
- Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
- Neural Poetry: Learning to Generate Poems using Syllables
- An Interaction Framework for Studying Co-Creative AI