Abstract: A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.

Abstract: A method includes accessing a web-based property over a network; storing a plurality of images or videos from the web-based property and associations between the plurality of images or videos and a target audience identifier responsive to the web-based property having a stored association with the target audience identifier; retrieving the plurality of images or videos from the database responsive to each of the plurality of images or videos having stored associations with the target audience identifier; executing a neural network to generate a performance score for each of the plurality of images or videos; calculating a target audience benchmark; executing the neural network to generate a first performance score for a first image or video and a second performance score for a second image or video; comparing the first performance score and the second performance score to the benchmark; and generating a record identifying the first image or video.

Abstract: A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.

Abstract: A method includes accessing a web-based property over a network; storing a plurality of images or videos from the web-based property and associations between the plurality of images or videos and a target audience identifier responsive to the web-based property having a stored association with the target audience identifier; retrieving the plurality of images or videos from the database responsive to each of the plurality of images or videos having stored associations with the target audience identifier; executing a neural network to generate a performance score for each of the plurality of images or videos; calculating a target audience benchmark; executing the neural network to generate a first performance score for a first image or video and a second performance score for a second image or video; comparing the first performance score and the second performance score to the benchmark; and generating a record identifying the first image or video.

Abstract: A method includes accessing a web-based property over a network; storing a plurality of images or videos from the web-based property and associations between the plurality of images or videos and a target audience identifier responsive to the web-based property having a stored association with the target audience identifier; retrieving the plurality of images or videos from the database responsive to each of the plurality of images or videos having stored associations with the target audience identifier; executing a neural network to generate a performance score for each of the plurality of images or videos; calculating a target audience benchmark; executing the neural network to generate a first performance score for a first image or video and a second performance score for a second image or video; comparing the first performance score and the second performance score to the benchmark; and generating a record identifying the first image or video.

Abstract: Systems, methods, and storage media for training a machine learning model are disclosed. Exemplary implementations may select a set of training images for a machine learning model, extract object features from each training image to generate an object tensor for each training image, extract stylistic features from each training image to generate a stylistic feature tensor for each training image, determine an engagement metric for each training image, and train a neural network comprising a plurality of nodes arranged in a plurality of sequential layers.

Abstract: Embodiments may: select a set of training images; extract a first set of features from each training image of the set of training images to generate a first feature tensor for each training image; extract a second set of features from each training image to generate a second feature tensor for each training image; reduce a dimensionality of each first feature tensor to generate a first modified feature tensor for each training image; reduce a dimensionality of each second feature tensor to generate a second modified feature tensor for each training image; construct a first generative model representing the first set of features and a second generative model representing the second set of features of the set of training images; identify a first candidate image; and apply a regression algorithm to the first candidate image and each of the first generative model and the second generative model to determine whether the first candidate image is similar to the set of training images.

Abstract: Systems, methods, and storage media for training a machine learning model are disclosed. Exemplary implementations may select a set of training images for a machine learning model, extract object features from each training image to generate an object tensor for each training image, extract stylistic features from each training image to generate a stylistic feature tensor for each training image, determine an engagement metric for each training image, and train a neural network comprising a plurality of nodes arranged in a plurality of sequential layers.

Abstract: Embodiments may: select a set of training images; extract a first set of features from each training image of the set of training images to generate a first feature tensor for each training image; extract a second set of features from each training image to generate a second feature tensor for each training image; reduce a dimensionality of each first feature tensor to generate a first modified feature tensor for each training image; reduce a dimensionality of each second feature tensor to generate a second modified feature tensor for each training image; construct a first generative model representing the first set of features and a second generative model representing the second set of features of the set of training images; identify a first candidate image; and apply a regression algorithm to the first candidate image and each of the first generative model and the second generative model to determine whether the first candidate image is similar to the set of training images.

Abstract: A method is disclosed. The method may include receiving an image or video; extracting a plurality of features from the image or video; executing a neural network using the plurality of features to obtain a performance score for the image or video, the neural network comprising an input layer, a plurality of intermediate layers subsequent to the input layer, and a regression layer or a classification layer; extracting values from one or more signals between an intermediate layer and the regression layer or the classification layer; for each of the plurality of features, calculating, based on at least one of the one or more values, an impact score indicating an impact the feature had on the performance score; and generating, based on one or more impact scores for the plurality of features, indications indicating an impact different features of the image or video had on the performance score.

Abstract: Systems and methods for implementing an artificial intelligence-powered smart gallery are provided. The smart gallery can be a software application that includes an ensemble of visual content-related features for end users. These features can include, but are not limited to, a set of user interactions to be performed on visual media or other content items, recommendations on and for a user's content items, analytical evaluations of a user's content items, as well as intelligent selection and optimization functions to enhance the performance of at least one of the user's content items. The presently disclosed systems can be integrated directly with an image management service or photo gallery that is part of a mobile operating system or other non-mobile software applications residing on a computing device.

Abstract: Embodiments may: select a set of training images; extract a first set of features from each training image of the set of training images to generate a first feature tensor for each training image; extract a second set of features from each training image to generate a second feature tensor for each training image; reduce a dimensionality of each first feature tensor to generate a first modified feature tensor for each training image; reduce a dimensionality of each second feature tensor to generate a second modified feature tensor for each training image; construct a first generative model representing the first set of features and a second generative model representing the second set of features of the set of training images; identify a first candidate image; and apply a regression algorithm to the first candidate image and each of the first generative model and the second generative model to determine whether the first candidate image is similar to the set of training images.

Abstract: Systems, methods, and storage media for training a machine learning model are disclosed. Exemplary implementations may select a set of training images for a machine learning model, extract object features from each training image to generate an object tensor for each training image, extract stylistic features from each training image to generate a stylistic feature tensor for each training image, determine an engagement metric for each training image, and train a neural network comprising a plurality of nodes arranged in a plurality of sequential layers.

Abstract: Systems and methods for implementing an artificial intelligence-powered smart gallery are provided. The smart gallery can be a software application that includes an ensemble of visual content-related features for end users. These features can include, but are not limited to, a set of user interactions to be performed on visual media or other content items, recommendations on and for a user's content items, analytical evaluations of a user's content items, as well as intelligent selection and optimization functions to enhance the performance of at least one of the user's content items. The presently disclosed systems can be integrated directly with an image management service or photo gallery that is part of a mobile operating system or other non-mobile software applications residing on a computing device.

Abstract: A method includes determining a recommended aspect for content that includes at least one image. The recommended aspect is determined at least in part based on activity data that indicates aspects of other content authored by or interacted with by a plurality of authors in at least one social network, website, application software, or mobile application software (app). The method further includes generating the content including the at least one image according to the recommended aspect. The recommended aspect is an aspect of the at least one image.

Abstract: Systems and methods for implementing an artificial intelligence-powered smart gallery are provided. The smart gallery can be a software application that includes an ensemble of visual content-related features for end users. These features can include, but are not limited to, a set of user interactions to be performed on visual media or other content items, recommendations on and for a user's content items, analytical evaluations of a user's content items, as well as intelligent selection and optimization functions to enhance the performance of at least one of the user's content items. The presently disclosed systems can be integrated directly with an image management service or photo gallery that is part of a mobile operating system or other non-mobile software applications residing on a computing device.

Abstract: A method includes determining a plurality of harvest content items. The harvest content items are ranked based on a performance metric. Matching criterion aspects of the harvest content items are determined. Aspects of a candidate content item are compared with the plurality of harvest content items according to the matching criterion aspects. A subset of the harvest content items that are similar to the candidate content item is determined. A transformation for the candidate content item is selected and applied to the candidate content item to generate a transformed content item.

Abstract: Systems and methods for implementing an artificial intelligence-powered smart gallery are provided. The smart gallery can be a software application that includes an ensemble of visual content-related features for end users. These features can include, but are not limited to, a set of user interactions to be performed on visual media or other content items, recommendations on and for a user's content items, analytical evaluations of a user's content items, as well as intelligent selection and optimization functions to enhance the performance of at least one of the user's content items. The presently disclosed systems can be integrated directly with an image management service or photo gallery that is part of a mobile operating system or other non-mobile software applications residing on a computing device.

Abstract: Systems, methods, and storage media for evaluating images are disclosed.

Abstract: A method includes determining a plurality of harvest content items. The harvest content items are ranked based on a performance metric. Matching criterion aspects of the harvest content items are determined. Aspects of a candidate content item are compared with the plurality of harvest content items according to the matching criterion aspects. A subset of the harvest content items that are similar to the candidate content item is determined. A transformation for the candidate content item is selected and applied to the candidate content item to generate a transformed content item.