Optimizing training data for image classification

Patent metadata

Field	Value
Publication number	US-11687841-B2
Application number	US-202016894344-A
Country	US
Kind code	B2
Filing date	Jun 5, 2020
Priority date	Jun 6, 2019
Publication date	Jun 27, 2023
Grant date	Jun 27, 2023

Abstract

A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.

First claim

What is claimed is:

1. A method for machine learning-based classification, the method comprising:
training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model;
generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model;
applying a clustering algorithm to the respective embeddings to generate a plurality of clusters of the embeddings;
identifying outlier embeddings from the plurality of clusters of the embeddings;
generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, the reduced training data set including the data points associated with embeddings in the plurality of clusters;
training the machine learning model with the reduced training data set to a second model state; and
applying the second model state to one or more data sets to classify the one or more data sets.

2. The method of claim 1 , wherein applying the second model state to classify one or more data sets comprises applying the second model state to classify one or more images.

3. The method of claim 1 , further comprising:
applying a distance learning algorithm to the respective embeddings to create a distanced embeddings set;
wherein applying a clustering algorithm to the respective embeddings comprises applying the clustering algorithm to the distanced embeddings set.

4. The method of claim 1 , wherein identifying outlier embeddings from the plurality of clusters of the embeddings comprises:
designating embeddings that are remote from all of the plurality of clusters as outlier embeddings.

5. The method of claim 1 , wherein identifying outlier embeddings from the plurality of clusters of the embeddings comprises:
designating embeddings that are remote from a single cluster of embeddings as outlier embeddings.

6. The method of claim 1 , wherein identifying outlier embeddings from the plurality of clusters of the embeddings comprises:
determining a respective category associated with each of the embeddings;
determining a respective category associated with each cluster of embeddings; and
designating embeddings that are remote from a cluster of embeddings associated with the category with which the embeddings are associated as outlier embeddings.

7. The method of claim 1 , wherein identifying outlier embeddings from the plurality of clusters of the embeddings comprises:
identifying at least a predetermined percentage of embeddings as outlier embeddings;
identifying at least a predetermined quantity of embeddings as outlier embeddings; or
identifying embeddings that are a predetermined distance from one of the plurality of clusters as outlier embeddings.

8. The method of claim 1 , wherein training the machine learning model with the reduced training data set comprises training the first model state of the machine learning model with the reduced training data set.

9. A system for machine learning-based classification, the system comprising:
a processor; and
a non-transitory, computer-readable memory storing instructions that, when executed by the processor, cause the processor to:
obtain training data comprising a full training data set;
train a machine learning model with the full training data set to a first model state;
generate respective embeddings for the data points in the full training data set with the first model state of the machine learning model;
apply a clustering algorithm to the respective embeddings to generate a plurality of clusters of the embeddings;
identify outlier embeddings from the plurality of clusters of the embeddings;
generate a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, the reduced training data set including the data points associated with embeddings in the plurality of clusters;
train the machine learning model with the reduced training data set to a second model state; and
apply the second model state to one or more data sets to classify the one or more data sets.

10. The system of claim 9 , wherein applying the second model state to classify one or more data sets comprises applying the second model state to classify one or more images.

11. The system of claim 9 , wherein the memory stores further instructions that, when executed by the processor, cause the processor to:
apply a distance learning algorithm to the respective embeddings to create a distanced embeddings set;
wherein applying a clustering algorithm to the respective embeddings comprises applying the clustering algorithm to the distanced embeddings set.

12. The system of claim 9 , wherein identifying outlier embeddings from the plurality of clusters of the embeddings comprises:
designating embeddings that are remote from all of the plurality of clusters as outlier embeddings.

13. The system of claim 9 , wherein identifying outlier embeddings from the plurality of clusters of the embeddings comprises:
designating embeddings that are remote from a single cluster of embeddings as outlier embeddings.

14. The system of claim 9 , wherein identifying outlier embeddings from the plurality of clusters of the embeddings comprises:
determining a respective category associated with each of the embeddings;
determining a respective category associated with each cluster of embeddings; and
designating embeddings that are remote from a cluster of embeddings associated with the category with which the embeddings are associated as outlier embeddings.

15. The system of claim 9 , wherein identifying outlier embeddings from the plurality of clusters of the embeddings comprises:
identifying at least a predetermined percentage of embeddings as outlier embeddings;
identifying at least a predetermined quantity of embeddings as outlier embeddings; or
identifying embeddings that are a predetermined distance from one of the plurality of clusters as outlier embeddings.

16. The system of claim 9 , wherein training the machine learning model with the reduced training data set comprises training the first model state of the machine learning model with the reduced training data set.

17. A machine learning-based method of classifying a plurality of images, the method comprising:
training a machine learning model with a full training data set, the full training data set comprising a plurality of paired images and classes, to generate a first model state of the machine learning model;
generating respective embeddings for the images in the full training data set with the first model state of the machine learning model;
applying a clustering algorithm to the respective embeddings to generate a plurality of clusters of the embeddings;
identifying outlier embeddings from the plurality of clusters of the embeddings;
generating a reduced training data set comprising the full training data set less the images associated with the outlier embeddings, the reduced training data set including the images associated with the embeddings in the plurality of clusters;
training the machine learning model with the reduced training data set to a second model state; and
applying the second model state to one or more unclassified images to classify the one or more unclassified images.

18. The method of claim 17 , wherein training the machine learning model with the reduced training data set comprises training the firs

Assignees

• Home Depot Product Authority Llc

Inventors

• Hagen Matthew
• Afshar Estelle
• Qu Huiming
• Ayadi Ala Eddine
• Wang Jiaqi

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06N20/20 Primary

  Ensemble learning · CPC title

• G06V10/763

  Non-hierarchical techniques, e.g. based on statistics of modelling distributions · CPC title