how to do incremental deep learning on data stream that can adapt to constantly generated data points?

Question

I am currently trying to learn a deep learning model on a data stream, which constantly generate new data points over time.  The goal is to generate a real-time DL model that can well adapt to newly generated data while does not affect the prediction for old data too much.  I am wondering if anyone know what might be the best way to do this "incremental deep learning on data stream"?  Any information would be much appreciated, thank you!

Saad · Answer

What you are describing does not require a change in the deep learning models you would use but a change in how to estimate them: you need an online estimation procedure rather than an off-line one. You can have a look at the seminal work of Botou [1998], or Chapter 8 of the book by Goodfellow et al. (2016).

Answered by Saad on March 28, 2021

how to do incremental deep learning on data stream that can adapt to constantly generated data points?

One Answer

Add your own answers!

Ask a Question