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Pros and cons of GAN evaluation measures: New developments
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Cites
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Cited by
A Survey on Generative Diffusion Models
Cao, H., Tan, C., Gao, Z., Xu, Y., Chen, G., Heng, P.-A., & Li, S. Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3361474 · Cited by 538
A Categorical Archive of ChatGPT Failures
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Self-citationA Categorical Archive of ChatGPT Failures
Borji, A. (2023) · doi:10.21203/rs.3.rs-2895792/v1 · Cited by 514
Self-citationGenerative Adversarial Networks in Time Series: A Systematic Literature Review
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Generative Models as an Emerging Paradigm in the Chemical Sciences
Anstine, D. M., & Isayev, O. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c13467 · Cited by 313
Metadata
- Authors
- Borji, A.
- Venue
- Computer Vision and Image Understanding
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 215
- Pages
- 103329
- ISSN
- 1077-3142 (ISSN-L)
- OpenAlex
- W3138478053
- Cited by
- 333
- References
- 22
- FWCI
- 18.43
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
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Data sources
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