Description

Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However,

Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-specify which part of the latent space captures specific factors of variations. While this is generally a hard problem because of the non-existence of analytical expressions to capture these variations, there are certain factors like geometric

5.33 MB application/pdf

Download count: 0

Details

Contributors
Date Created
  • 2019
Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: M.S., Arizona State University, 2019
      Note type
      thesis
    • Includes bibliographical references
      Note type
      bibliography
    • Field of study: Electrical engineering

    Citation and reuse

    Statement of Responsibility

    by Kaushik Koneripalli Seetharam

    Machine-readable links