I want to use Google AutoML vision API for image classification, but with an incremental learning setup - more specifically I should be able to incrementally provide new training data with possibly brand new (and previously unknown) class labels. For example, lets say I train the network today for three labels: A, B and C. Now, after a week, I want to add some new data labeled with a brand new class D. And then after another week, I want to add even newer data labeled with a brand new class E. At this point, the model should be able to classify an input image into any of those five classes, with each incremental addition to the model causing very little accuracy drop.
Is that possible with google AutoML vision API?
Currently you could keep importing new data into existing AutoML dataset and each week train a new model. There is import API and train API.
The assumption of causing very little accuracy drop may be unrealistic. There may valid cases when adding new label will make the accuracy go down. E.g. add labels that are hard to distinguish from previous labels or adding labels without performing data cleanup (adding label and not applying it to existing images in which objects with this label are visible).