Outline

Problem Statement

• Gathering thermal comfort related data (or in general user-generated data) is costly and takes time.
• Hard to generalize over a big population of people, as well as obtain a similar number of responses for each label/category (e.g. Thermal comfort labels, always a predominance in the 'comfort' class and not the rest)

Methods

• Try different generative models for augment datasets
  • GAN (and its variations: CGAN, WGAN, WCGAN, TGAN, TableGAN)
  • Autoencoders (and the variations: Adversarial, variational)

Evaluation metrics

• Baseline: Original train and test set
• Train set and synthetic data as training set: Should increase the performance since classes would be more balanced
• Synthetic data as training set: Performance should be comparable with the baseline showing the synthetic set captures the same characteristics as the real train set