Exploring the Capabilities and Limitations of Generative Networks: A Comprehensive Study
Keywords:
Generative Networks, Autoencoders, Autoregressive models, PixelCNN, Overfitting, Inception ScoreAbstract
Generative Networks are a type of deep learning models that can generate realistic and novel data samples. They are commonly employed for a number of things, like as the production of music, the discovery of novel medications, and the production of pictures and words. Generative Networks have demonstrated impressive capabilities, but they also face several limitations and challenges in training and evaluation. Therefore, ongoing research and development are required to address these issues and enhance their performance. Future research should focus on improving the scalability, interpretability, and robustness of Generative Networks, as well as exploring new directions such as cross-domain and multi-modal generation. Moreover, it is crucial to consider the ethical implications of Generative Networks and implement responsible practices for their development and deployment.
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