US 12,277,753 B1
Common-sense bias discovery and mitigation for machine-learning tasks
Gaurav Bharaj, San Francisco, CA (US); Miao Zhang, Jersey City, NJ (US); Zee Fryer, Oakland, CA (US); Ben Colman, New York, NY (US); and Ali Shahriyari, Las Vegas, NV (US)
Assigned to Reality Defender, Inc., New York, NY (US)
Filed by Reality Defender, Inc., New York, NY (US)
Filed on Jun. 21, 2024, as Appl. No. 18/751,166.
Claims priority of provisional application 63/600,577, filed on Nov. 17, 2023.
Int. Cl. G06V 10/774 (2022.01); G06V 10/75 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/774 (2022.01) [G06V 10/751 (2022.01); G06V 10/82 (2022.01)] 17 Claims
OG exemplary drawing
 
1. A method for reducing bias in a training image dataset for training a machine-learning model, comprising:
receiving a plurality of text strings comprising at least one text string describing each image in the training image dataset;
generating a plurality of embeddings based on the plurality of text strings;
identifying, based on the plurality of embeddings, a plurality of visual features in the training image dataset;
identifying one or more correlations between the plurality of visual features in the training image dataset;
receiving a user input identifying at least one biased correlation from the one or more correlations; and
training the machine-learning model at least partially by adjusting one or more data sampling weights associated with one or more training images in the training image dataset based on the user input.