FOCUSED AND GAMIFIED ACTIVE LEARNING FOR MACHINE LEARNING CORPORA DEVELOPMENT
The present disclosure describes techniques and systems to provide focused and gamified active learning for machine learning model development. The present disclosure describes determining an active learning algorithm with which to choose batches of content that correspond to specific categories of content to be annotated. Furthermore, the present disclosure provides that the batches of content, and particularly characteristics of the content can be identified for annotation based on ML model performance, such as an entropy of the ML model.
1 . A computer implemented method, comprising:
executing a machine learning (ML) model to infer a classification of a plurality of classifications for a plurality of content items managed by a content management system (CMS);
determining an entropy of the ML model for each of the plurality of classifications;
identifying a plurality of additional content items managed by the CMS to be annotated based on the one of the plurality of classifications with the largest entropy;
identifying at least one characteristic associated with the additional content items, the at least one characteristic a subset of a plurality of possible characteristics associated with the additional content items;
sending a first information element, to a user device, the first information element comprising indications of the plurality of additional content items and the at least one characteristic to be annotated;
receiving, from the user device, an indication of a value of the at least one characteristic for each the plurality of additional content items;
augmenting a corpora of samples with the additional content items and an indication of the value of the at least one characteristic;
generating a training dataset from the corpora of samples; and
retraining the ML model with the training dataset.
2 . The computer implemented method of claim 1 , wherein the plurality of content items and the additional content items are images and where the at least one characteristic is an item represented in the images.
3 . (canceled)
4 . The computer implemented method of claim 1 , comprising:
sending a second information element to the user device, the second information element comprising indications of information to be included in a user interface comprising instructions to annotate ones of the plurality of additional content items associated with the one of the plurality of classifications with the largest entropy.
5 . The computer implemented method of claim 4 , comprising:
predicting an affect of the annotated at least one characteristic of the plurality of additional content items on training of the ML model; and
sending a third information element to the user device, the third information element comprising indications of the affect.
6 . The computer implemented method of claim 4 , comprising:
identifying a plurality of secondary additional content items managed by the CMS to be annotated based on the classification of the plurality of content items; and
sending a fourth information element, to a second user device, the fourth information element comprising indications of the plurality of additional content items and the at least one characteristic to be annotated.
7 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:
execute a machine learning (ML) model to infer a classification of a plurality of classifications for a plurality of content items managed by a content management system (CMS);
determine an entropy of the ML model for each of the plurality of classifications;
identify a plurality of additional content items managed by the CMS to be annotated based on the one of the plurality of classifications with the largest entropy;
identify at least one characteristic associated with the additional content items, the at least one characteristic a subset of a plurality of possible characteristics associated with the additional content items;
send a first information element, to a user device, the first information element comprising indications of the plurality of additional content items and the at least one characteristic to be annotated;
receive, from the user device, an indication of a value of the at least one characteristic for each the plurality of additional content items;
augment a corpora of samples with the additional content items and an indication of the value of the at least one characteristic;
generate a training dataset from the corpora of samples; and
retrain the ML model with the training dataset.
8 . The computer-readable storage medium of claim 7 , wherein the plurality of content items and the additional content items are images and where the at least one characteristic is an item represented in the images.
9 . (canceled)
10 . The computer-readable storage medium of claim 7 , the instructions, when executed by the computer further cause the computer to:
send a second information element to the user device, the second information element comprising indications of information to be included in a user interface comprising instructions to annotate ones of the plurality of additional content items associated with the one of the plurality of classifications with the largest entropy.
11 . The computer-readable storage medium of claim 10 , the instructions, when executed by the computer further cause the computer to:
predict an affect of the annotated at least one characteristic of the plurality of additional content items on training of the ML model; and
send a third information element to the user device, the third information element comprising indications of the affect.
12 . The computer-readable storage medium of claim 10 , the instructions, when executed by the computer further cause the computer to:
identify a plurality of secondary additional content items managed by the CMS to be annotated based on the classification of the plurality of content items; and
send a fourth information element, to a second user device, the fourth information element comprising indications of the plurality of additional content items and the at least one characteristic to be annotated.
13 . A computing apparatus, comprising:
a content management server, comprising: a processor; and memory storing instructions that, when executed by the processor, cause the apparatus to:
execute a machine learning (ML) model to infer a classification of a plurality of classifications for a plurality of content items managed by a content management system (CMS),
determine an entropy of the ML model for each of the plurality of classifications,
identify a plurality of additional content items managed by the CMS to be annotated based on the one of the plurality of classifications with the largest entropy,
identify at least one characteristic associated with the additional content items, the at least one characteristic a subset of a plurality of possible characteristics associated with the additional content items,
send a first information element, to a user device, the first information element comprising indications of the plurality of additional content items and the at least one characteristic to be annotated,
receive, from the user device, an indication of a value of the at least one characteristic for each the plurality of additional content items,
augment a corpora of samples with the additional content items and an indication of the value of the at least one characteristic,
generate a training dataset from the corpora of samples, and
retrain the ML model with the training dataset; and
a data storage device, storing the plurality of content items and the plurality of additional content.
14 . The computing apparatus of claim 13 , wherein the one or more content items and the additional content items are images and where the at least one characteristic is an item represented in the images.
15 . (canceled)
16 . The computing apparatus of claim 13 , the instructions, when executed by the processor further cause the apparatus to:
send a second information element to the user device, the second information element comprising indications of information to be included in a user interface comprising instructions to annotate ones of the plurality of additional content items associated with the one of the plurality of classifications with the largest entropy.
17 . The computing apparatus of claim 16 , the instructions, when executed by the processor further cause the apparatus to:
predict an affect of the annotated at least one characteristic of the plurality of additional content items on training of the ML model; and
send a third information element to the user device, the third information element comprising indications of the affect.
18 . The computing apparatus of claim 17 , the instructions, when executed by the processor further cause the apparatus to:
identify a plurality of secondary additional content items managed by the CMS to be annotated based on the classification of the one or more content items; and
send a fourth information element, to a second user device, the fourth information element comprising indications of the plurality of additional content items and the at least one characteristic to be annotated.
19 . The computing apparatus of claim 18 , the instructions, when executed by the processor further cause the apparatus to:
predict an additional affect of the annotated at least one characteristic of the plurality of secondary additional content items on training of the ML model; and
send a fifth information element to the second user device second user device, the fifth information element comprising an indication of the affect contrasted with the additional affect.
20 . The computing apparatus of claim 13 , the content management server comprising a network interface to couple to a network, wherein the user device is addressable via the network.