I am trying to implement incremental/online learning for a facial recognition application. I've trained a model on a dataset and it works perfectly fine, however, I need to capture new faces(classes) over time and add them to the existing dataset. Is there any way that I can train the model with new classes without retraining from the scratch?
I've not found any rich resources so far and really appreciated if anyone can point me out somewhere.
Niloufar Abaei Please try searching for things like "transfer learning", or "pre-trained models"
Here is an introduction: https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124751
Basically, there is a class of neural networks where most of the layers except the last few ones are generic and could be trained to be used even if you add a new class, or several ones. So, I guess you can just replace and train the last layer for your new classes. Hope this helps.
Thank you Amitava Banerjee . Actually, I've tried this idea, however, the training time progressively goes up by adding new classes over time. Because I have to retrain the last four layers for all classes not only the new one.
You might want to look into the Creme library for online learning: https://github.com/creme-ml/creme.
A tutorial for this on a vision task with Keras can also be found here: https://www.pyimagesearch.com/2019/06/17/online-incremental-learning-with-keras-and-creme/
Thanks, Tarek Naous . Unfortunately, Creme doesn't support what I'm looking for (based on what the developer of Creme told me).
I believe the only thing you can do is to use your already trained model and fine-tune it on the new samples once they become available. As the number of classes increases you will, for sure, need to retrain all classification layers based on the new increased number of classes. At this point, increased computational time and cost is inevitable during training. Not aware of any proposed technique to mitigate this cost, unless training was parallelized with hardware.
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