This project is an exploration of Deep Convolutional Generative Adversarial Networks (DCGANs) trained on the CIFAR-10 dataset. The objective of this project is to generate images that resemble the images from the CIFAR-10 dataset by leveraging the adversarial training paradigm. DCGANs represent a significant step forward in the ability of deep neural networks to produce high-quality, realistic images unsupervised.
- Implementation of DCGAN architecture as described in the seminal DCGAN paper.
- Training and evaluation on the CIFAR-10 dataset.
- Visualization of the training process and generated images.
- Analysis of the model's performance and image quality over time.
Upon completion of the training, the script will generate and save images that the Generator model produces. These images will showcase how the model's ability to generate realistic images evolves throughout the training process.