US 2023/0145919 A1Published application

Method and apparatus for class incremental learning

AssigneeSamsung Electronics Co., Ltd.

InventorsMete OZAY, Marco Toldo

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Abstract

The present application generally relates to a method for training a machine learning, ML, model using class incremental learning, and to a computer-implemented method and apparatus for using the trained machine learning, ML, model. The method may learn how to update semantic representations of old concepts (classes) by modelling drift of semantic representations. The method may also learn how to update feature representations of old concepts (classes) by modelling drift of feature representations

Claims16

Dependent claims

A computer-implemented method for training a classifier using class incremental learning, CIL, wherein the classification model is a machine learning, ML, model comprising a feature extraction model and a classification model and wherein at each incremental time step in the CIL a new training dataset having a set of samples with class labels is obtained and an optimisation over a plurality of stages is applied, the training method comprising for each incremental step:

extracting, using the feature extraction model, a first set of features representing the samples within the new training set at the beginning of the incremental step and a second set of features representing the samples within the training set at a subsequent stage within the optimisation;
obtaining a first set of old class prototypes for the beginning of the incremental step, wherein each class prototype is a semantic representation of the sets of features of the samples which have the same class label and wherein each old class prototype represents old samples which are samples that are not present in the new training set;
learning a feature drift model using the extracted first and second sets of features, wherein the feature drift model estimates the drift between the features representing each sample as the classification model is updated;
learning a semantic drift model using the set of old class prototypes and first set of features, wherein the semantic drift model estimates the drift between the class prototypes for each class as the classification model is updated;
inferring at least one of a second set of old class prototypes and a set of old features using the learnt feature drift model and the learnt semantic drift model, wherein the second set of prototypes are semantic representations of revived old samples at a subsequent stage within the optimisation and the set of old features are the feature representations of revived old samples at the subsequent stage within the optimisation;
and
using at least one of the inferred second set of old class prototypes and set of old features to update the ML model;
and
when the final incremental step is completed, outputting the trained classification model.

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Abstract

extracting, using the feature extraction model, a first set of features representing the samples within the new training set at the beginning of the incremental step and a second set of features representing the samples within the training set at a subsequent stage within the optimisation;
obtaining a first set of old class prototypes for the beginning of the incremental step, wherein each class prototype is a semantic representation of the sets of features of the samples which have the same class label and wherein each old class prototype represents old samples which are samples that are not present in the new training set;
learning a feature drift model using the extracted first and second sets of features, wherein the feature drift model estimates the drift between the features representing each sample as the classification model is updated;
learning a semantic drift model using the set of old class prototypes and first set of features, wherein the semantic drift model estimates the drift between the class prototypes for each class as the classification model is updated;
inferring at least one of a second set of old class prototypes and a set of old features using the learnt feature drift model and the learnt semantic drift model, wherein the second set of prototypes are semantic representations of revived old samples at a subsequent stage within the optimisation and the set of old features are the feature representations of revived old samples at the subsequent stage within the optimisation;
and
using at least one of the inferred second set of old class prototypes and set of old features to update the ML model;
and
when the final incremental step is completed, outputting the trained classification model.

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