Generating Synthetic Time-Series Data for Cow Activity Traces

Synthetic data allows studying and sharing of potential sensitive data without the risk of a privacy breach or leak. Synthetic data is generated by a model, which was trained on real data and learns its statistical properties. Thus, generating synthetic data can also be adapted and used for the prediction of missing values or the detection of outliers.

Training of such a model is usually done by using a Generative Adversarial Network (GAN) or a Variational Autoencoder (VAE).

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Student Target Groups:

  • Students in ICE and Computer Science

Thesis Type:

  • Master Thesis (Duration: 6 months)

Goal:

  • Thorough literature research on the topic.
  • Implement a generative machine learning model
  • Evaluate the results of this generative model
  • Summarize the results in a written report, present and demonstrate the prototype
     

Requirements: 

  • Creativity, interest in state-of-the-art machine learning methods.
  • Programming skills in Python.
  • Knowledge of in deep learning frameworks (TensorFlow/Keras or PyTorch) is recommended, but not mandatory

Start:

  • a.s.a.p.

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