Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices.

Abstract: A system can include one or more processors and one or more non-transitory computer-readable storage media storing computing instructions configured to run on the one or more processors and perform: generating a training dataset for training a neural network detection model; 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 utilizing the neural network detection model to determine whether the at least one image comprises the non-compliant content. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: generating a training dataset comprising synthetic training images for training a neural network detection model to identify non-compliant content in images; executing a training procedure that utilizes the synthetic training images to train the neural network detection model to identify the non-compliant content; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image includes the non-compliant content. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving an image at a first-level analysis component comprising a first neural network structure; analyzing, using the first neural network structure of the first-level analysis component, the image to determine an image category associated with the image; selecting at least one second-level analysis component that is associated with the image category to analyze the image; analyzing, using a second neural network structure associated with the at least one second-level analysis component that was selected, the image to determine whether the image includes non-compliant content; and in response to determining that the image includes non-compliant content, executing a corrective measure. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: generating a training dataset comprising synthetic training images for training a neural network detection model to identify non-compliant content in images; executing a training procedure that utilizes the synthetic training images to train the neural network detection model to identify the non-compliant content; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image includes the non-compliant content. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of: receiving an image at a first-level analysis component comprising a first neural network structure; analyzing, using the first neural network structure of the first-level analysis component, the image to determine an image category associated with the image; selecting at least one second-level analysis component that is associated with the image category to analyze the image; analyzing, using a second neural network structure associated with the at least one second-level analysis component that was selected, the image to determine whether the image includes non-compliant content; and in response to determining that the image includes non-compliant content, executing a corrective measure. Other embodiments are disclosed herein.

Abstract: A product image evaluation system. In embodiments, the system comprises or interacts with a product database comprising a product information record that comprises a product identifier and a product category for a product, and an image database comprising a plurality of candidate images for the product. In embodiments the image database can comprise images received from a plurality of different sources. The system can comprise a parameterized grouping engine configured to separate images into groups of similar images, an image selector configured to select one or more images from each group, and an image sorter configured to determine an order of the selected images. Embodiments can distill the superset of all available images to provide a set of images that are “sufficiently different” from each other and satisfy quality requirements. As a result, no images containing unique information are left behind, and images containing duplicate or irrelevant information are discarded.

Abstract: A multi-modal computer classification network system for use in classifying data records is described herein. The system includes a memory device, a first classification computer server, a second classification computer server, and a policy computer server. The memory device includes an item records database and a labeling database. The first classification computer server includes a first classifier program that is configured to select an item record from the item database and generate a first classification record including a first ranked list of class labels. The second classification computer server includes a second classifier program that is configured to generate a second classification record including a second ranked list of class labels. The policy computer server includes a policy network that is programmed to determine a predicted class label based on the first and second ranked lists of class labels.

Abstract: The present disclosure relates to system(s) and method(s) for real time compression of a data frame. The system receives the data frame comprising a set of symbols. Further, the system identifies frequency of each symbol, from the set of symbols. The system further sorts the symbols in descending order of frequency, associated with each symbols. Further, the system computes a compression gain associated with each predefined case type, a set of predefined case types. Furthermore, the system selects a target predefined case type, based on the comparison of the compression gain of each predefined case types. The system further assigns a compressed code to Most Frequent Symbols (MFS), in the data frame. The compressed code is assigned based on the target predefined case type. Further, the system generates a compressed frame, associated with the data frame. The compressed frame comprises a header and a sequence of compressed symbols.

Abstract: The present disclosure relates to system(s) and method(s) for real time compression of a data frame. The system receives the data frame comprising a set of symbols. Further, the system identifies frequency of each symbol, from the set of symbols. The system further sorts the symbols in descending order of frequency, associated with each symbols. Further, the system computes a compression gain associated with each predefined case type, a set of predefined case types. Furthermore, the system selects a target predefined case type, based on the comparison of the compression gain of each predefined case types. The system further assigns a compressed code to Most Frequent Symbols (MFS), in the data frame. The compressed code is assigned based on the target predefined case type. Further, the system generates a compressed frame, associated with the data frame. The compressed frame comprises a header and a sequence of compressed symbols.

Abstract: A multi-modal computer classification network system for use in classifying data records is described herein. The system includes a memory device, a first classification computer server, a second classification computer server, and a policy computer server. The memory device includes an item records database and a labeling database. The first classification computer server includes a first classifier program that is configured to select an item record from the item database and generate a first classification record including a first ranked list of class labels. The second classification computer server includes a second classifier program that is configured to generate a second classification record including a second ranked list of class labels. The policy computer server includes a policy network that is programmed to determine a predicted class label based on the first and second ranked lists of class labels.

Abstract: A system for use in monitoring an operation of a classification model in generating estimated labels for item records is described herein. The system receives a current labeling budget value including a number of trusted labels available for use in a labeling operation, determines a current selection probability for each item record included in an item list as a function of the current labeling budget value, selects a plurality of item records from the item list as a function of each corresponding current selection probability, and generates a sampling list including the selected item records. The system determines a risk measurement value associated with the classification model indicating an accuracy of the estimated labels as compared to trusted labels associated with the item records.

Abstract: In one embodiment, a method includes accessing information about an advertiser's product catalog. The method may further include accessing an information graph that contains nodes and edges connecting the nodes. The nodes in the graph represent concepts related to at least one vertical. The method further includes associating a first node in the graph with a second node in the graph based on a relationship between the meaning of the concept represented by the first node and the meaning of the concept represented by the second node. A portion of the graph corresponds to the advertiser's product catalog. The method may further include enriching information associated with the portion of the product catalog with information related to the concepts and relationships represented by the portion of the graph corresponding to the portion of the product catalog of the advertiser.

Abstract: In one embodiment, a method includes accessing an information graph that contains nodes and edges connecting the nodes. The nodes in the graph represent concepts related to at least one vertical. The method further includes associating a first node in the graph with a second node in the graph based on a relationship between the meaning of the concept represented by the first node and the meaning of the concept represented by the second node. A portion of the graph corresponds to an advertiser's product catalog. The method may further include accessing an ad library that contains elements for an advertisement. The method may further include selecting at least one of the elements based on the portion of the graph corresponding to the portion of the product catalog of the advertiser. The method may further include generating an advertisement for display based on the selected elements.

Abstract: In one embodiment, a method includes receiving, from an advertiser, an identification of one or more products in a catalog of the advertiser. The method may further include accessing an information graph that contains nodes and edges connecting the nodes. The nodes in the graph represent concepts related to at least one vertical. The method further includes associating a first node in the graph with a second node in the graph based on a relationship between the meaning of the concept represented by the first node and the meaning of the concept represented by the second node. A portion of the graph corresponds to an advertiser's product catalog. The method may further include identifying, based on the portion of the graph corresponding to the products identified by the advertiser, one or more products or product types related to the products identified by the advertiser.

Abstract: In one embodiment, a method includes creating an information graph that contains nodes and edges connecting the nodes. The nodes in the graph represent concepts related to at least one vertical. The method further includes associating a first node in the graph with a second node in the graph based on a relationship between the meaning of the concept represented by the first node and the meaning of the concept represented by the second node.

Abstract: In one embodiment, a method includes accessing an information graph that contains nodes and edges connecting the nodes. The nodes in the graph represent concepts related to at least one vertical. The method further includes associating a first node in the graph with a second node in the graph based on a relationship between the meaning of the concept represented by the first node and the meaning of the concept represented by the second node. A portion of the graph corresponds to an advertiser's product catalog. The method may further include accessing one or more targeting criteria for one or more products in the portion of the product catalog. The method may further include creating at least a portion of an advertising campaign, for at least one product for which targeting criteria has been accessed, based on the targeting criteria and the portion of the graph corresponding to the portion of the product catalog.

Abstract: In one embodiment, a method includes constructing an intent map for a plurality of products, the intent map comprising intent topics and each intent topic comprising intents, and then deriving a plurality of keywords from the intent map based on keyword templates.