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Overview
Variational Deep Learning is a field where neural networks are trained to generate data such as images, videos etc. The networks are trained on existing data and learn it’s features to generate new data.
Implementations
- Vanilla Autoencoder
- Denoising Autoencoder
- Sparse Autoencoder
- Contractive Autoencoder
- Variational Autoencoder
- Deep Convolutional Generative Adversarial Network
- Conditional Generative Adversarial Network
The Autoencoders were trained on the MNIST dataset and the GANs on the CIFAR-10 dataset.
Vanilla Autoencoder
Denoising Autoencoder
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Variational Autoencoder
Deep Convolutional General Adversarial Network:
Conditional General Adversarial Network:
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Team
- Pulkit Mathur
- Atharva Kathale
- Appari Shanti
- Sibam Parida
- Sushant Jha
- Vignesh Srinivas
- Kalyani Sainis
- Rishika Bhagwatkar
- Khurshed Fitter
- Saketh Bachu
- Siddharth Singh
