US 12,682,617 B1Granted

Techniques for improving data efficiency in image-based machine learning

AssigneeBentley Systems, Incorporated

InventorsOscar Ozdemir, Marc-André Gardner, Karl-Alexandre Jahjah, Marc-André Lapointe, Louis-Philippe Asselin

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Abstract

In example embodiments, a dataset labeling software process combines diversity selection and with one or more uncertainty selection techniques in a multi-stage process that produces a labeled training dataset for training an image-based ML model. In an initial stage, diversity selection alone may be used to seed a labeled training dataset. In a subsequent stage, diversity selection may be combined with a first procedure for computing uncertainty (e.g., uncertainty selection based on ensemble learning) to build the labeled training dataset until a first stopping condition is met. Optionally, in a still further stage, diversity selection may be combined with a second, different procedure for computing uncertainty (e.g., uncertainty selection based on Monte Carlo (MC) dropout) to further build the labeled training dataset until a second stopping condition is met.

Claims20

Dependent claims

A method for training an image-based machine learning (ML) model, comprising:

in an initial stage,
computing, by a software process executing on one or more computing devices, a dissimilarity between each image of an initial set of images available for training, and
selecting, by the software process, a first sub-set of images from the initial set of images based on the dissimilarity from each other, labeling the first sub-set of images, and adding the labeled first sub-set of images to a labeled training dataset;
training one or more image-based ML models on the labeled training dataset;
in a subsequent stage,
computing, by the software process, an uncertainty of the one or more image-based ML models for remaining images of the initial set of images, and
selecting, by the software process, a second sub-set of images from the remaining images of the initial set of images based on a combination of the dissimilarity from each other and the uncertainty of the one or more image-based ML models, labeling the second sub-set of images, and adding the second sub-set of images to the labeled training dataset;
repeating the training the one or more image-based ML models, and the computing the uncertainty of the one or more image-based ML models, and the selecting the second sub-set of images of the subsequent stage until a stopping condition is met to build the labeled training dataset;
and
using the labeled training dataset to train the image-based ML model.

7 more elements

A non-transitory computing device readable medium having instructions stored thereon, the instructions when executed by one or more computing devices operable to:

in an initial stage,
compute a dissimilarity between each image of an initial set of images in available for training, and
select a first sub-set of images from the initial set of images based on the dissimilarity from each other, label the first sub-set of images, and add the labeled first sub-set of images to a labeled training dataset;
in a subsequent stage,
compute an uncertainty of one or more image-based machine learning (ML) models for each image of the initial set of images, and
select a second sub-set of images from the initial set of images based on a combination of the dissimilarity from each other and the uncertainty, label the second sub-set of images, and add the sub-set of images to the labeled training dataset;
repeat the computation of the uncertainty the selection of the sub-set of images of the subsequent stage until a stopping condition is met to build the labeled training dataset;
and
use the labeled training dataset to train an image-based ML model.

6 more elements

A computing device configured to train an image-based machine learning (ML) model, comprising:

one or more processors;
and
a memory configured to store software for execution on the one or more processors, the software including a data labeling software process that is configured to:
in an initial stage,
compute a dissimilarity between each image of an initial set of images available for training, and
select a first sub-set of images from the initial set of images based on the dissimilarity from each other;
provide the first sub-set of images for training one or more image-based ML models;
in a subsequent stage,
compute an uncertainty of the one or more image-based ML models for remaining images of the initial set of images, and
select a second sub-set of images from the remaining images of the initial set of images based on a combination of the dissimilarity from each other and the uncertainty of the one or more image-based ML models;
provide the second sub-set of images for training the one or more image-based ML models;
and
repeat the computation of the uncertainty of the one or more image-based ML models, and the selection of the second sub-set of images of the subsequent stage, and the provision of the second sub-set of images for training until a stopping condition is met.

10 more elements

Dependencies are inferred from the claim text.

Facts

Application: 18/537,438Filed 12 Dec 2023
Granted: 14 Jul 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification9 codes

G06V 10/774
Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or…:…
G06F 18/217
Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation: validation; Performance…
G06F 18/2413
Relating to the classification model, e.g. parametric or non-parametric approaches: based on distances to training or reference patterns
G06V 10/761
Image or video pattern matching; Proximity measures in feature spaces: proximity, similarity or dissimilarity measures