Building a multiscale variational autoencoder (m-vae).
It is similar to a wavelet decomposition with a learnable encoding in the middle.
It creates different scale representation of an image and then encodes it into z-domain.
The downscaled versions are used to learn high level features and the higher level versions encode minute details.
The model allows to create as much as log_2(input_size) numbers of levels.
I intend to use this m-vae as a building block for Classifiers, Fuzzers, Anomaly Detection and more.
- Build basic model
- Abstract Encoding and Decoding
- CIFAR10 notebook
- MNIST notebook
- Generator model
- Classifier model
- Anomaly detection
Trained on the CIFAR10 dataset to recreate images for 10 epochs.
Cifar10 - Epoch 1
Cifar10 - Epoch 150
