DCGANs can be customized for different use cases. The various practical applications of DCGANs include the following:
- The generation of anime characters: Currently, animators manually draw characters with computer software and sometimes on paper as well. This is a manual process that usually takes a lot of time. With DCGANs, new anime characters can be generated in much less time, hence improving the creative process.
- The augmentation of datasets: If you want to train a supervised machine learning model, to train a good model, you would require a large dataset. DCGANs can help by augmenting the existing dataset, therefore increasing the size of the dataset required for supervised model training.
- The generation of MNIST characters: The MNIST dataset contains 60,000 images of handwritten digits. To train a complex supervised learning model, the...
