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| Објашњиви ГАН (Explainable GAN)× | Generativna suparnička mreža× | |
|---|---|---|
| Oblast | Duboko učenje | Duboko učenje |
| Porodica | Machine learning | Machine learning |
| Godina nastanka≠ | 2019 (GAN Dissection); ongoing | 2014 |
| Tvorac≠ | Bau, D. et al. (GAN Dissection); broader XAI-GAN community | Goodfellow, I. et al. |
| Tip≠ | Explainable generative model | Generative deep learning (adversarial two-network game) |
| Temeljni izvor≠ | Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. (2019). GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR 2019). link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Drugi nazivi | XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Srodne | 4 | 4 |
| Sažetak≠ | Explainable GAN applies interpretability techniques to Generative Adversarial Networks to reveal which internal units and latent directions cause specific visual or structural features in generated outputs. It combines GAN training with post-hoc analysis tools — such as unit dissection, saliency maps, or disentangled latent spaces — to make generative model behaviour transparent and auditable. | 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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