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    GENERATING AN IMPROVED NAMED ENTITY RECOGNITION MODEL USING NOISY DATA WITH A SELF-CLEANING DISCRIMINATOR MODEL
    12.
    发明申请
    GENERATING AN IMPROVED NAMED ENTITY RECOGNITION MODEL USING NOISY DATA WITH A SELF-CLEANING DISCRIMINATOR MODEL 有权

    公开(公告)号：US20250103813A1

    公开(公告)日：2025-03-27

    申请号：US18472746

    申请日：2023-09-22

    Applicant: Adobe Inc.

    Inventor： Ruiyi Zhang ,  Zhendong Chu ,  Vlad Morariu ,  Tong Yu ,  Rajiv Jain ,  Nedim Lipka ,  Jiuxiang Gu

    IPC: G06F40/295

    Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that train a named entity recognition (NER) model with noisy training data through a self-cleaning discriminator model. For example, the disclosed systems utilize a self-cleaning guided denoising framework to improve NER learning on noisy training data via a guidance training set. In one or more implementations, the disclosed systems utilize, within the denoising framework, an auxiliary discriminator model to correct noise in the noisy training data while training an NER model through the noisy training data. For example, while training the NER model to predict labels from the noisy training data, the disclosed systems utilize a discriminator model to detect noisy NER labels and reweight the noisy NER labels provided for training in the NER model.

    EFFICIENT VISION-LANGUAGE RETRIEVAL USING STRUCTURAL PRUNING
    14.
    发明申请
    EFFICIENT VISION-LANGUAGE RETRIEVAL USING STRUCTURAL PRUNING 有权

    公开(公告)号：US20250013866A1

    公开(公告)日：2025-01-09

    申请号：US18347877

    申请日：2023-07-06

    Applicant: ADOBE INC.

    Inventor： Handong Zhao ,  Yue Bai ,  Zhe Lin ,  Ajinkya Gorakhnath Kale ,  Jiuxiang Gu ,  Tong Yu ,  Sungchul Kim

    IPC: G06N3/082 ,  G06N3/04

    Abstract: Systems and methods for reducing inference time of vision-language models, as well as for multimodal search, are described herein. Embodiments are configured to obtain an embedding neural network. The embedding neural network is pretrained to embed inputs from a plurality of modalities into a multimodal embedding space. Embodiments are further configured to perform a first progressive pruning stage, where the first progressive pruning stage includes a first pruning of the embedding neural network and a first fine-tuning of the embedding neural network. Embodiments then perform a second progressive pruning stage based on an output of the first progressive pruning stage, where the second progressive pruning stage includes a second pruning of the embedding neural network and a second fine-tuning of the embedding neural network.

    DETECTION AND INTERPRETATION OF LOG ANOMALIES
    15.
    发明公开
    DETECTION AND INTERPRETATION OF LOG ANOMALIES 审中-公开

    公开(公告)号：US20240311221A1

    公开(公告)日：2024-09-19

    申请号：US18120773

    申请日：2023-03-13

    Applicant: Adobe Inc.

    Inventor： Jaeho Bang ,  Sungchul Kim ,  Ryan A. Rossi ,  Tong Yu ,  Handong Zhao

    IPC: G06F11/07 ,  G06N20/00

    CPC classification number: G06F11/0769 ,  G06F11/0778 ,  G06N20/00

    Abstract: In implementations of systems for detection and interpretation of log anomalies, a computing device implements an anomaly system to receive input data describing a two-dimensional representation of log templates and timestamps. The anomaly system processes the input data using a machine learning model trained on training data to detect anomalies in two-dimensional representations of log templates and timestamps. A log anomaly is detected in the two-dimensional representation using the machine learning model based on processing the input data. The anomaly system generates an indication of an interpretation of the log anomaly for display in a user interface based on a log template included in the two-dimensional representation.

    UTILIZING A GENERATIVE NEURAL NETWORK TO INTERACTIVELY CREATE AND MODIFY DIGITAL IMAGES BASED ON NATURAL LANGUAGE FEEDBACK
    16.
    发明公开
    UTILIZING A GENERATIVE NEURAL NETWORK TO INTERACTIVELY CREATE AND MODIFY DIGITAL IMAGES BASED ON NATURAL LANGUAGE FEEDBACK 审中-公开

    公开(公告)号：US20230230198A1

    公开(公告)日：2023-07-20

    申请号：US17576091

    申请日：2022-01-14

    Applicant: Adobe Inc.

    Inventor： Ruiyi Zhang ,  Yufan Zhou ,  Christopher Tensmeyer ,  Jiuxiang Gu ,  Tong Yu ,  Tong Sun

    IPC: G06T3/00 ,  G06T11/00 ,  G10L15/22 ,  G10L15/26 ,  G06N3/04

    CPC classification number: G06T3/0056 ,  G06T11/00 ,  G10L15/22 ,  G10L15/26 ,  G06N3/04 ,  G10L2015/223

    Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a neural network framework for interactive multi-round image generation from natural language inputs. Specifically, the disclosed systems provide an intelligent framework (i.e., a text-based interactive image generation model) that facilitates a multi-round image generation and editing workflow that comports with arbitrary input text and synchronous interaction. In particular embodiments, the disclosed systems utilize natural language feedback for conditioning a generative neural network that performs text-to-image generation and text-guided image modification. For example, the disclosed systems utilize a trained model to inject textual features from natural language feedback into a unified joint embedding space for generating text-informed style vectors. In turn, the disclosed systems can generate an image with semantically meaningful features that map to the natural language feedback. Moreover, the disclosed systems can persist these semantically meaningful features throughout a refinement process and across generated images.

    DIALOGUE SKELETON ASSISTED PROMPT TRANSFER FOR DIALOGUE SUMMARIZATION
    17.
    发明申请
    DIALOGUE SKELETON ASSISTED PROMPT TRANSFER FOR DIALOGUE SUMMARIZATION 有权

    公开(公告)号：US20250028751A1

    公开(公告)日：2025-01-23

    申请号：US18355901

    申请日：2023-07-20

    Applicant: Adobe Inc.

    Inventor： Tong Yu ,  Kaige Xie ,  Haoliang Wang ,  Junda Wu ,  Handong Zhao ,  Ruiyi Zhang ,  Kanak Vivek Mahadik ,  Ani Nenkova

    IPC: G06F16/34 ,  G06F40/35

    Abstract: Dialogue skeleton assisted prompt transfer for dialogue summarization techniques are described that support training of a language model to perform dialogue summarization in a few-shot scenario. A processing device, for instance, receives a training dataset that includes training dialogues. The processing device then generates dialogue skeletons based on the training dialogues using one or more perturbation-based probes. The processing device trains a language model using prompt transfer between a source task, e.g., dialogue state tracking, and a target task, e.g., dialogue summarization, using the dialogue skeletons as supervision. The processing device then receives an input dialogue and uses the trained language model to generate a summary of the input dialogue.

    TABULAR DATA MACHINE-LEARNING MODELS
    19.
    发明公开
    TABULAR DATA MACHINE-LEARNING MODELS 审中-公开

    公开(公告)号：US20240152771A1

    公开(公告)日：2024-05-09

    申请号：US17979843

    申请日：2022-11-03

    Applicant: Adobe Inc.

    Inventor： Can Qin ,  Sungchul Kim ,  Tong Yu ,  Ryan A. Rossi ,  Handong Zhao

    IPC: G06N5/02

    CPC classification number: G06N5/02

    Abstract: Tabular data machine-learning model techniques and systems are described. In one example, common-sense knowledge is infused into training data through use of a knowledge graph to provide external knowledge to supplement a tabular data corpus. In another example, a dual-path architecture is employed to configure an adapter module. In an implementation, the adapter module is added as part of a pre-trained machine-learning model for general purpose tabular models. Specifically, dual-path adapters are trained using the knowledge graphs and semantically augmented trained data. A path-wise attention layer is applied to fuse a cross-modality representation of the two paths for a final result.

    TEACHING A MACHINE CLASSIFIER TO RECOGNIZE A NEW CLASS
    20.
    发明公开
    TEACHING A MACHINE CLASSIFIER TO RECOGNIZE A NEW CLASS 审中-公开

    公开(公告)号：US20230143721A1

    公开(公告)日：2023-05-11

    申请号：US17524282

    申请日：2021-11-11

    Applicant: ADOBE INC.

    Inventor： Sungchul Kim ,  Subrata Mitra ,  Ruiyi Zhang ,  Rui Wang ,  Handong Zhao ,  Tong Yu

    IPC: G06F40/295 ,  G06N20/00

    CPC classification number: G06F40/295 ,  G06N20/00

    Abstract: Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.

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