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A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Vision

arXiv 2311.00995 ↗First posted 2 Nov 2023cs.CV eess.IVcited by 0

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Abstract (arXiv)

Generative Adversarial Networks (GANs) have been workhorse generative models for last many years, especially in the research field of computer vision. Accordingly, there have been many significant advancements in the theory and application of GAN models, which are notoriously hard to train, but produce good results if trained well. There have been many a surveys on GANs, organizing the vast GAN literature from various focus and perspectives. However, none of the surveys brings out the important chronological aspect: how the multiple challenges of employing GAN models were solved one-by-one over time, across multiple landmark research works. This survey intends to bridge that gap and present some of the landmark research works on the theory and application of GANs, in chronological order.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 68 of its 85 references that are in the map. First posted on arXiv 2 Nov 2023.

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References · 68

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38 more
  1. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
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  3. 2017Improved Training of Wasserstein GANscited by 2,223
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