Optimizing training data for image classification

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 first model state of the machine learning model with the reduced training data set.

19. The method of claim 17 , 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; or

designating embeddings that are remote from a single respective cluster of embeddings as outlier embeddings.

20. The method of claim 17 , 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.