公开(公告)号：US20210319056A1

公开(公告)日：2021-10-14

申请号：US16843218

申请日：2020-04-08

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Ajinkya Kale ,  Xiaowei Jia ,  Zhe Lin

IPC: G06F16/43 ,  G06F16/45 ,  G06F16/438 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to a retrieval method including: generating a graph representing a set of users, items, and queries; generating clusters from the media items; generating embeddings for each cluster from embeddings of the items within the corresponding cluster; generating augmented query embeddings for each cluster from the embedding of the corresponding cluster and query embeddings of the queries; inputting the cluster embeddings and the augmented query embeddings to a layer of a graph convolutional network (GCN) to determine user embeddings of the users; inputting the embedding of the given user and a query embedding of the given query to a layer of the GCN to determine a user-specific query embedding; generating a score for each of the items based on the item embeddings and the user-specific query embedding; and presenting the items having the score exceeding a threshold.

公开(公告)号：US20200327446A1

公开(公告)日：2020-10-15

申请号：US16380566

申请日：2019-04-10

Applicant: Adobe Inc.

Inventor： Xiaowei Jia ,  Sheng Li ,  Handong Zhao ,  Sungchul Kim

IPC: G06N20/00

Abstract: Techniques are disclosed for the generation of adversarial training data through sequence perturbation, for a deep learning network to perform event sequence analysis. A methodology implementing the techniques according to an embodiment includes applying a long short-term memory attention model to an input data sequence to generate discriminative sequence periods and attention weights associated with the discriminative sequence periods. The attention weights are generated to indicate the relative importance of data in those discriminative sequence periods. The method further includes generating perturbed data sequences based on the discriminative sequence periods and the attention weights. The generation of the perturbed data sequences employs selective filtering or conservative adversarial training, to preserve perceptual similarity between the input data sequence and the perturbed data sequences. The input data sequence may be created by vectorizing a temporal input data stream comprising words, symbols, and the like, into a multidimensional vectorized numerical data sequence format.

公开(公告)号：US11681737B2

公开(公告)日：2023-06-20

申请号：US16843218

申请日：2020-04-08

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Ajinkya Kale ,  Xiaowei Jia ,  Zhe Lin

IPC: G06F16/43 ,  G06F16/45 ,  G06F16/438 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F16/43 ,  G06F16/438 ,  G06F16/45 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to a retrieval method including: generating a graph representing a set of users, items, and queries; generating clusters from the media items; generating embeddings for each cluster from embeddings of the items within the corresponding cluster; generating augmented query embeddings for each cluster from the embedding of the corresponding cluster and query embeddings of the queries; inputting the cluster embeddings and the augmented query embeddings to a layer of a graph convolutional network (GCN) to determine user embeddings of the users; inputting the embedding of the given user and a query embedding of the given query to a layer of the GCN to determine a user-specific query embedding; generating a score for each of the items based on the item embeddings and the user-specific query embedding; and presenting the items having the score exceeding a threshold.

公开(公告)号：US11507878B2

公开(公告)日：2022-11-22

申请号：US16380566

申请日：2019-04-10

Applicant: Adobe Inc.

Inventor： Xiaowei Jia ,  Sheng Li ,  Handong Zhao ,  Sungchul Kim

IPC: G06N20/00

Abstract: Techniques are disclosed for the generation of adversarial training data through sequence perturbation, for a deep learning network to perform event sequence analysis. A methodology implementing the techniques according to an embodiment includes applying a long short-term memory attention model to an input data sequence to generate discriminative sequence periods and attention weights associated with the discriminative sequence periods. The attention weights are generated to indicate the relative importance of data in those discriminative sequence periods. The method further includes generating perturbed data sequences based on the discriminative sequence periods and the attention weights. The generation of the perturbed data sequences employs selective filtering or conservative adversarial training, to preserve perceptual similarity between the input data sequence and the perturbed data sequences. The input data sequence may be created by vectorizing a temporal input data stream comprising words, symbols, and the like, into a multidimensional vectorized numerical data sequence format.