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公开(公告)号：US10785318B2
公开(公告)日：2020-09-22
申请号：US15793001
申请日：2017-10-25
Applicant: Adobe Inc.
Inventor： Sunny Dhamnani , Vishwa Vinay , Lilly Kumari , Ritwik Sinha
IPC: G06F15/173 , H04L29/08 , H04L29/06 , G06K9/62
Abstract: A session identification system classifies network sessions with a network application as either human-generated or generated by a non-human, such as by a bot. In an embodiment, the session identification system receives a set of unlabeled network sessions, and determines a label for a single class of the unlabeled network sessions. Based on the one-class labeling information, the session identification system determines multiple subsets of the unlabeled network sessions. Multiple classifiers included in the session identification system generate probabilities describing each of the unlabeled network sessions. The session identification system classifies each of the unlabeled network sessions based on a combination of the generated probabilities.
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公开(公告)号：US20190325616A1
公开(公告)日：2019-10-24
申请号：US15994837
申请日：2018-05-31
Applicant: Adobe Inc.
Inventor： Niyati Himanshu Chhaya , Nitin Rathor , Lilly Kumari , Vineet Vinayak Pasupulety , Rutuj Jugade
Abstract: Makeup identification using deep learning in a digital medium environment is described. Initially, a user input is received to provide a digital image depicting a face which has a desired makeup characteristic. A discriminative neural network is trained to identify and describe makeup characteristics of the input digital image based on data describing differences in visual characteristics between pairs of images, which include a first image depicting a face with makeup applied and a second image depicting a face without makeup applied. The makeup characteristics identified by the discriminative neural network are displayed for selection to search for similar digital images that have the selected makeup characteristic. Once retrieved, the similar digital images can be displayed along with the input digital image having the desired makeup characteristic.
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公开(公告)号：US10755447B2
公开(公告)日：2020-08-25
申请号：US15994837
申请日：2018-05-31
Applicant: Adobe Inc.
Inventor： Niyati Himanshu Chhaya , Nitin Rathor , Lilly Kumari , Vineet Vinayak Pasupulety , Rutuj Jugade
Abstract: Makeup identification using deep learning in a digital medium environment is described. Initially, a user input is received to provide a digital image depicting a face which has a desired makeup characteristic. A discriminative neural network is trained to identify and describe makeup characteristics of the input digital image based on data describing differences in visual characteristics between pairs of images, which include a first image depicting a face with makeup applied and a second image depicting a face without makeup applied. The makeup characteristics identified by the discriminative neural network are displayed for selection to search for similar digital images that have the selected makeup characteristic. Once retrieved, the similar digital images can be displayed along with the input digital image having the desired makeup characteristic.
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4.发明申请公开(公告)号：US20190356684A1
公开(公告)日：2019-11-21
申请号：US15982393
申请日：2018-05-17
Applicant: Adobe Inc.
Inventor： Ritwik Sinha , Vishwa Vinay , Sunny Dhamnani , Margarita Savova , Lilly Kumari , David Weinstein
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.
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