Adobe, Inc.

Meta Platforms, Inc.

Vladimir Leonid Bychkovsky

Sylvain Philippe Paris

Jen-Chan Jeff Chien

Frederic Durand

Eric Chan

Saurav Mohapatra

James Cipar

Alvin F Wen

Lili Hu

In one embodiment, a method includes receiving a request to determine whether to perform an action, wherein the action is based on one or more feature values, generating a prediction of whether to perform the action, wherein the prediction is generated using a machine-learning model that is trained based on the feature values, a heuristic value based on the feature values, and one or more feedback scores based on corresponding past predictions generated by the machine-learning model, where the heuristic value indicates whether to perform the action based on one or more predetermined conditions that are based on the feature values, performing the action when the prediction indicates that the action is to be performed, receiving a feedback score that indicates a level of effectiveness of the prediction, and updating the machine-learning model based on the feedback score, the feature values, and the heuristic value.

Machine learning in resource-constrained environments

Techniques are disclosed relating to modifying an automatically predicted adjustment. In one embodiment, the automatically predicted adjustment may be adjusted, for example, based on a rule. The automatically predicted adjustment may be based on a machine learning prediction. A new image may be globally adjusted based on the modified automatically predicted adjustment.

Automatic image adjustment parameter correction

Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

Image adjustment

Techniques are disclosed relating to generating generic labels, translating generic labels to image pipeline-specific labels, and automatically adjusting images. In one embodiment, generic labels may be generated. Generic algorithm parameters may be generated based on training a regression algorithm with the generic labels. The generic labels may be translated to pipeline-specific labels, which may be usable to automatically adjust an image.

Growing community of inventors

Cambridge, MA, United States of America

Vladimir Leonid Bychkovsky