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| Datenerweiterung× | Generative Adversarial Network× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2019 | 2014 |
| Urheber≠ | Connor Shorten & Taghi Khoshgoftaar | Goodfellow, I. et al. |
| Typ≠ | Regularization / data preprocessing technique | Generative deep learning (adversarial two-network game) |
| Wegweisende Quelle≠ | Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6, 60. DOI ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Aliasnamen | Training Data Augmentation, Image Augmentation, Veri Artırma, Synthetic Data Augmentation | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Verwandt≠ | 2 | 4 |
| Zusammenfassung≠ | Data augmentation is a family of techniques that artificially expands a training dataset by applying label-preserving transformations to existing samples. Originally systematized for image classification tasks, it is now applied broadly across vision, text, audio, and tabular domains. It emerged as a practical answer to the chronic scarcity of labeled data in supervised deep learning and remains a standard preprocessing step in modern neural network pipelines. | A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation. |
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