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公开(公告)号：US11521221B2
公开(公告)日：2022-12-06
申请号：US15909723
申请日：2018-03-01
Applicant: Adobe Inc.
Inventor： Shiv Kumar Saini , Vishwa Vinay , Vaibhav Nagar , Aishwarya Mittal
IPC: G06Q30/02 , G06N3/04 , G06N7/00 , G06F16/9535
Abstract: This disclosure involves predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks. For example, a method includes a processing device performing operations including accessing input data for an entity and transforming the input data into a dense vector entity representation representing the entity. Transforming the input data includes applying, to the input data, a neural network including simultaneously trained propensity models. Each propensity model predicts a different task based on the input data. Transforming the input data also includes extracting the dense vector entity representation from a common layer of the neural network to which the propensity models are connected. The operations performed by the processing device include computing a predicted behavior by applying a predictive model to the dense vector entity representation and transmitting the predicted behavior to a computing device that customizes a presentation of electronic content at a remote user device.
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公开(公告)号：US20230090313A1
公开(公告)日：2023-03-23
申请号：US17483295
申请日：2021-09-23
Applicant: Adobe Inc.
Inventor： Aishwarya Mittal , Sachin Beniwal , Abhishek Kumar Pandey
Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for implementing content-aware filters to autonomously remove scan marks from digital documents. In particular implementations, the disclosed systems utilize a set of targeted scan mark models in a scan mark removal pipeline. For example, each scan mark model includes a corresponding content-aware filter configured to identify document regions that match a designated class of scan marks to filter. Examples of scan mark models include staple scan mark models, punch hole scan mark models, and page turn scan mark models. In certain embodiments, the disclosed systems then use the scan mark models to generate mark-specific masks based on document input features. Additionally, in some embodiments, the disclosed systems combine the mark-specific masks into a final segmentation mask and apply the final segmentation mask to the digital document for correcting the identified regions with scan marks.
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3.发明申请公开(公告)号：US20190272553A1
公开(公告)日：2019-09-05
申请号：US15909723
申请日：2018-03-01
Applicant: Adobe Inc.
Inventor： Shiv Kumar Saini , Vishwa Vinay , Vaibhav Nagar , Aishwarya Mittal
Abstract: This disclosure involves predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks. For example, a method includes a processing device performing operations including accessing input data for an entity and transforming the input data into a dense vector entity representation representing the entity. Transforming the input data includes applying, to the input data, a neural network including simultaneously trained propensity models. Each propensity model predicts a different task based on the input data. Transforming the input data also includes extracting the dense vector entity representation from a common layer of the neural network to which the propensity models are connected. The operations performed by the processing device include computing a predicted behavior by applying a predictive model to the dense vector entity representation and transmitting the predicted behavior to a computing device that customizes a presentation of electronic content at a remote user device.
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