Generative machine learning models for image synthesis: Advances, challenges, and future directions
Generative adversarial networks (GANs) are popular models that can learn distributions and high-dimensional data such as images, audio, and texts. While these models have high representational capacity, training them can be difficult. Mode collapse, non-convergence, sensitivity to hyperparameters, a...
Gorde:
| Egile Nagusiak: | , , |
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| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
Elsevier
2026-06-01
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| Saila: | Machine Learning with Applications |
| Gaiak: | |
| Sarrera elektronikoa: | http://www.sciencedirect.com/science/article/pii/S2666827026000605 |
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