Synthetic Observability Data Generation using GANs | AI/ML in 5G Challenge

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AI for Good

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Published on August 30, 2022 12:00:00 AM ● Video Link: https://www.youtube.com/watch?v=5rzwsEBh94w

Duration: 0:00

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Generative modeling is an unsupervised learning technique in machine learning. It involves automatically discovering, and learning, the patterns in the input data. Once learns, it can be used to generate new examples that would be similar to the original dataset. Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods. The challenge (Synthetic Observability Data Generation using GANs) proposes to use GANs for generating infrastructure time-series data (data that has time dependency). Using GANs to generate datasets that should preserve temporal dynamics is challenging compared to generating Images, for example.

This talk will provide a detailed overview of GANs, covering some hands-on exercises using Tensorflow. The talk will also include explanation of GANs for time-series Data, taking TGANs as example. The talk will begin with a quick survey of existing GANs for Time-Series Data, and end with a discussion on possible GANs to consider for this challenge.

🎙 Speakers:
Rohit Singh Rathaur
Researcher
BIT Ranchi

🎙 Moderators:
Miya Nishio
Student
University of Tokyo

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The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.