Stabilizing and Improving Training of Generative Adversarial Networks through Identity Blocks and Modified Loss Function
Generative adversarial networks (GANs) are a powerful tool for synthesizing realistic images, but they can be difficult to train and are prone to instability and mode collapse. This paper proposes a new model called Identity Generative Adversarial Network (IGAN) that addresses these issues. This mod...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
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| Subjects: | |
| Online Access: | View Fulltext in Publisher View in Scopus |