Implementation of a few types of Generative Adversarial Networks in Keras.
- Train an ACGAN on the MNIST dataset using class priors. Inputs are (100) dimensional noise vectors along with (10) dimensional class vectors (conditioning) and the outputs are generated (1,28,28) images
- Samples of generated outputs at various epochs in
ACGAN-MNIST/Run1/Results - Trained generator and discriminator models in
ACGAN-MNIST/Run1/Models - Generated samples in
ACGAN-MNIST/Samples - The samples generated are much sharper than other generative methods such as variational autoencoders etc.
- Some cherry picked generations below
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- Train a DCGAN on the MNIST dataset without any class priors. Inputs are (100) dimensional noise vectors and the outputs are generated (1,28,28) images
- Samples of generated outputs in
DCGAN-MNIST/GeneratedOutputs - Trained generator and discriminator models in
DCGAN-MNIST/TrainedModels - Generated digit samples below
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Note: Training the GAN for longer would give us much better results, which are not as blurry.