Systems, methods, and techniques for training neural networks and utilizing the neural networks to detect non-compliant content

1. A system comprising:

one or more processors; and

one or more non-transitory computer-readable storage media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform:

generating a training dataset for training a neural network detection model, comprising:

generating, using a transformation algorithm, synthetic training images by (i) applying one or more random transformations on a first set of images comprising non-compliant content and (ii) appending the non-compliant content to a second set of compliant images; and

utilizing the transformation algorithm by (i) applying the one or more random transformations on edge case training images and (ii) appending the edge case training images to one or more compliant images in the second set of compliant images;

identifying, using the neural network detection model, as trained, the non-compliant content in the synthetic training images;

receiving, at the neural network detection model, at least one image; and

determining, using the neural network detection model, as trained, whether the at least one image comprises the non-compliant content.

2. The system of claim 1 , wherein generating the training dataset for training the neural network detection model further comprises:

receiving the first set of images comprising the non-compliant content;

receiving the second set of compliant images; and

receiving the edge case training images to tune an accuracy of the neural network detection model.

3. The system of claim 1 , wherein generating the training dataset for training the neural network detection model further comprises

automatically generating and storing annotation information associated with the synthetic training images; and

utilizing the annotation information to train the neural network detection model during a training procedure.

4. The system of claim 3 , wherein the training procedure comprises applying active learning to train the neural network detection model.

5. The system of claim 1 , wherein generating the training dataset further comprises converting the first set of images to transparent images.

6. The system of claim 1 , wherein the non-compliant content comprises one or more of:

logos or badges identifying one or more brands;

logos or badges identifying one or more seller statuses;

logos or badges comprising marketing information; or

logos or badges identifying one or more countries.

7. The system of claim 1 , wherein applying the one or more random transformations comprises one or more of:

randomly scaling the non-compliant content before appending the non-compliant content to the second set of compliant images;

randomly rotating the non-compliant content before appending the non-compliant content to the second set of compliant images;

randomly distorting the non-compliant content before appending the non-compliant content to the second set of compliant images; or

randomly determining where the non-compliant content is to be placed on the second set of compliant images.

8. The system of claim 1 , wherein:

the edge case training images comprise compliant images that resemble one or more categories of the non-compliant content.

9. The system of claim 1 , wherein the computing instructions, when executed on the one or more processors, further cause the one or more processors to perform:

executing one or more corrective measures on the at least one image in response to determining that the at least one image comprises the non-compliant content, wherein the one or more corrective measures comprise removing the non-compliant content from the at least one image.

10. The system of claim 9 , wherein the one or more corrective measures comprises at least one of:

removing the at least one image from a database;

preventing the at least one image from being published on a website;

flagging the at least one image for manual review;

deleting the at least one image;

removing the non-compliant content from the at least one image; or

annotating the at least one image to block the non-compliant content.

11. A method being implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising:

generating a training dataset for training a neural network detection model comprising:

generating, using a transformation algorithm, synthetic training images by (i) applying one or more random transformations on a first set of images comprising non-compliant content and (ii) appending the non-compliant content to a second set of compliant images; and

utilizing the transformation algorithm by (i) applying the one or more random transformations on edge case training images and (ii) appending the edge case training images to one or more compliant images in the second set of compliant images;

identifying, using the neural network detection model, as trained, the non-compliant content in the synthetic training images;

receiving, at the neural network detection model, at least one image; and

determining, using the neural network detection model, as trained, whether the at least one image comprises the non-compliant content.

12. The method of claim 11 , wherein generating the training dataset for training the neural network detection model comprises:

receiving the first set of images comprising the non-compliant content;

receiving the second set of compliant images; and

receiving the edge case training images to tune an accuracy of the neural network detection model.

13. The method of claim 11 , wherein generating the training dataset for training the neural network detection model further comprises:

automatically generating and storing annotation information associated with the synthetic training images; and

utilizing the annotation information to train the neural network detection model during a training procedure.

14. The method of claim 13 , wherein the training procedure comprises applying active learning to train the neural network detection model.

15. The method of claim 11 , wherein generating the training dataset further comprises converting the first set of images to transparent images.

16. The method of claim 11 , wherein the non-compliant content comprises one or more of:

logos or badges identifying one or more brands;

logos or badges identifying one or more seller statuses;

logos or badges comprising marketing information; or

logos or badges identifying one or more countries.

17. The method of claim 11 , wherein applying the one or more random transformations comprises one or more of:

randomly scaling the non-compliant content before appending the non-compliant content to the second set of compliant images;

randomly rotating the non-compliant content before appending the non-compliant content to the second set of compliant images;

randomly distorting the non-compliant content before appending the non-compliant content to the second set of compliant images; or

randomly determining where the non-compliant content is to be placed on the second set of compliant images.

18. The method of claim 11 , wherein:

the edge case training images comprise compliant images that resemble one or more categories of the non-compliant content.

19. The method of claim 11 , further comprises:

executing one or more corrective measures on the at least one image in response to determining that the at least one image comprises the non-compliant content, wherein the one or more corrective measures comprise removing the non-compliant content from the at least one image.

20. The method of claim 19 , wherein the one or more corrective measures comprises at least one of:

removing the at least one image from a database;

preventing the at least one image from being published on a website;

flagging the at least one image for manual review;

deleting the at least one image;

removing the non-compliant content from the at least one image; or

annotating the at least one image to block the non-compliant content.