Samsung patents on-device image sorting that learns new items without forgetting

1. A computer-implemented method of generating a classification machine learning (ML) model for deployment to a user device, the method comprising: obtaining a backbone ML feature embedding model for generating feature embedding vectors for images, wherein the backbone ML feature embedding model is associated with a set of base classes and each base class in the set of base classes has a base class prototype in the form of a feature embedding vector which is representative of the base class; obtaining a training dataset comprising a plurality of input images each of which has a label corresponding to a base class from the set of base classes; obtaining a generative ML model; generating, using multiple input images in the training dataset, training data for the generative ML model by: for each input image in the multiple input images: extracting, using the backbone ML feature embedding model, an input feature embedding vector for the input image; selecting the prototype feature embedding vector corresponding to the base class label for the input image; calculating a residual vector comprising a distance and a direction between the extracted input feature embedding vector and the selected prototype feature embedding vector in a feature space which is defined by input feature embedding vectors extracted for the multiple input images; and associating the calculated residual vector with the selected prototype feature embedding vector as an instance of the training data for the generative ML model; training, using the training data comprising calculated residual vectors and associated prototype feature embedding vectors, the generative ML model to generate a reconstructed residual vector; and outputting the classification ML model, comprising the backbone ML feature embedding model and the trained generative ML model, to a user device.