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Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation.

Frantzeska Lavda1,2, Alexandros Kalousis1

  • 1Geneva School of Business Administration (DMML Group), University of Applied Sciences and Arts Western Switzerland (HES-SO), 1227 Geneva, Switzerland.

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Summary
This summary is machine-generated.

This study introduces BtVAE, a novel machine learning method enabling models to generalize to new situations and generate out-of-distribution data. It addresses challenges in combinatorial generalization and reduces the need for extensive labeled data.

Keywords:
VAEback translationgenerative models

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Machine learning models struggle with out-of-distribution (OOD) generalization and combinatorial generalization.
  • Acquiring high-quality labeled data is often costly and time-consuming, especially for specialized tasks.

Purpose of the Study:

  • To propose BtVAE, a method that leverages conditional Variational Autoencoder (VAE) models.
  • To enable combinatorial generalization and semi-supervised generation of OOD data.

Main Methods:

  • Utilizes conditional VAE models for semi-supervised learning.
  • Achieves generalization by recombining existing attributes in novel ways, rather than introducing new factors of variation.

Main Results:

  • Demonstrates the ability to achieve combinatorial generalization in specific scenarios.
  • Successfully generates OOD data by applying learned attributes to unseen combinations.

Conclusions:

  • BtVAE offers a promising approach to enhance machine learning model adaptability and data generation capabilities.
  • The method effectively addresses limitations in generalization and data labeling costs.