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Self-Supervised Variational Auto-Encoders.

Ioannis Gatopoulos1,2, Jakub M Tomczak2

  • 1Institute of Informatics, Universiteit van Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.

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Summary
This summary is machine-generated.

We introduce self-supervised Variational Auto-Encoders (selfVAEs), a novel generative model for density estimation and data generation. This approach simplifies objectives and enhances data compression by using discrete transformations.

Keywords:
deep generative modelingdeep learningnon-learnable transformationsprobabilistic modeling

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Density estimation, data compression, and generation are key AI tasks.
  • Variational Auto-Encoders (VAEs) offer a unified framework for these tasks.
  • Existing VAEs can be complex to optimize.

Purpose of the Study:

  • Introduce a novel class of generative models: self-supervised Variational Auto-Encoders (selfVAEs).
  • Simplify the VAE objective function and enable conditional/unconditional sampling.
  • Explore the application of selfVAEs in data compression and reconstruction.

Main Methods:

  • Utilized deterministic and discrete data transformations (e.g., downscaling, edge detection) as latent variables.
  • Developed both single and hierarchical selfVAE architectures.
  • Evaluated performance on benchmark image datasets: Cifar10, Imagenette64, and CelebA.

Main Results:

  • SelfVAEs simplify the objective function compared to standard VAEs.
  • Hierarchical selfVAE architectures demonstrate benefits over single-transformation models.
  • Achieved effective data reconstruction, enabling memory-quality trade-offs in compression.

Conclusions:

  • SelfVAEs offer a flexible and simplified approach to generative modeling.
  • The model shows promise for advanced data compression and reconstruction tasks.
  • Demonstrated the efficacy of selfVAEs on diverse image datasets.