1. Variational autoencoders to visualize non-intuitive data
Sebastian Fellner, Radu Burcea, Endre Wollan and Mathias Minos-Stensrud
Supervisors: Enrique and Bruno

2. Main objective
Use variational autoencoder (VAE) to produce visualizations from any temporal dataset.
Produce a sequence of images in respect to the relevant structure of the dataset.
First step: Generate a sequence of MNIST-images that reflects the data of a provided audio file, e.g. amplitude and frequency.
→ Resulting in a video with images and audio
Image:
Image:

3. Method Normal VAE Data: MNIST
Modularism: encode and decode are overwritten by subclass

4. Method Normal VAE Data: MNIST
Modularism: encode and decode are overwritten by subclass

5. Preliminary results
Sweep 200hz hz

10. Next step Train VAE on new dataset, CelebA
Generate more relevant images in respect to the input data, e.g. audio.
Mel-frequency cepstral coefficients (MFCC) (bottom image)
MusicVAE
Different types of temporal data
Emotional speech
Stock prices
Formel 1 driving data
Brain signals
S. Yang, P. Luo, C. C. Loy, and X. Tang, "From Facial Parts Responses to Face Detection: A Deep Learning Approach", in IEEE International Conference on Computer Vision (ICCV), 2015