US 11,790,049 B1
Techniques for improving machine-learning accuracy and convergence
Gaurav Dhir, Bothell, WA (US); Ankit Sirmorya, Bothell, WA (US); and Ying Li, Falls Church, VA (US)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Mar. 31, 2021, as Appl. No. 17/218,759.
Int. Cl. G06F 18/40 (2023.01); G06F 3/0481 (2022.01); G06N 20/00 (2019.01); G06N 5/04 (2023.01); G06F 3/04842 (2022.01); G06V 10/40 (2022.01); G06F 18/22 (2023.01); G06V 30/14 (2022.01)
CPC G06F 18/40 (2023.01) [G06F 3/0481 (2013.01); G06F 3/04842 (2013.01); G06F 18/22 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06V 10/40 (2022.01); G06V 30/14 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
obtaining, by a computing device, a machine-learning model that has been previously trained to identify an image vector from an input image;
obtaining, by the computing device, a set of image feature vectors based at least in part on providing individual images of a set of images to the machine-learning model as input, the set of images comprising varying images of an item;
calculating, by the computing device, a similarity score for each pair of image feature vectors from the set of image feature vectors obtained from the machine-learning model;
presenting, at a user interface, the similarity score for each pair of image feature vectors from the set of image feature vectors;
receiving, via the user interface, user input indicating removal of a specific image from the set of images;
in response to the user input, removing the image from the set of images;
updating the user interface based at least in part on removing the image from the set of images; and
conducting at least one user interface experiment utilizing the set of images as updated.