Project 2: (Autoencoders, GANs and DRL)
Project 2: (Autoencoders, GANs and DRL)
Image Generation using Variational Autoencoder and Generative Adversarial Networks
The project activity is designed to give you hands-on experience in using Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs) to generate new images from an existing dataset. The goal of the project is to train VAE and GAN models on a chosen dataset of images, generate new images using these models, and compare the quality and diversity of the generated images from both models.
Goal:
The goal of this project is to use Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs) to generate new images from an existing dataset.
Guidelines:
- Dataset: First, you need to choose a dataset of images, such as CIFAR-10, MNIST, or any other dataset of your choice. This dataset will be used to train and test the VAE and GAN models.
- Preprocessing: After selecting the dataset, you will need to preprocess the images by resizing them to a standard size and converting them to grayscale or RGB format, depending on the model used. This step ensures that all the images have the same size and format, making it easier to train and test the models.
- Variational Autoencoder (VAE): Next, you will implement a VAE to generate new images. The VAE should take an image as input, encode it into a lower-dimensional representation, and then decode it to generate a new image. The VAE model will be trained on the preprocessed dataset to learn how to generate new images.
- Generative Adversarial Networks (GANs): Similarly, you will implement a GAN to generate new images. The GAN should consist of a generator network and a discriminator network. The generator network should take random noise as input and generate a new image, while the discriminator network should distinguish between real and fake images. The GAN model will also be trained on the preprocessed dataset to learn how to generate new images.
- Image Generation: Once both the VAE and GAN models are trained, you can generate new images using these models. You can compare the quality and diversity of the generated images from both models and observe the differences in the generated images.
- Evaluation: To evaluate the performance of the models, you can use metrics such as mean squared error, peak signal-to-noise ratio, and structural similarity index. These metrics can help you understand how well the models are able to generate new images that are similar to the original dataset. You can also visualize and compare the generated images from both models to understand the strengths and weaknesses of each model.
Optionally, you can experiment with different architectures and hyperparameters of the VAE and GAN models to improve the performance and quality of the generated images. This step can help you gain a deeper understanding of how these models work and how you can customize them to suit your specific needs.
Overall, this project activity provides a great opportunity to gain hands-on experience in implementing and training deep learning models for image generation. By the end of this project, you will have a better understanding of how VAEs and GANs work, how to evaluate their performance, and how to apply them in various fields.