公开(公告)号：US20220269946A1

公开(公告)日：2022-08-25

申请号：US17375728

申请日：2021-07-14

Applicant: salesforce.com, inc.

Inventor： Pan Zhou ,  Caiming Xiong ,  Chu Hong Hoi

IPC: G06N3/08 ,  G06N3/04

Abstract: Embodiments described herein provide a contrastive learning mechanism with self-labeling refinement, which iteratively employs the network and data themselves to generate more accurate and informative soft labels for contrastive learning. Specifically, the contrastive learning framework includes a self-labeling refinery module to explicitly generate accurate labels, and a momentum mix-up module to increase similarity between a query and its positive, which in turn implicitly improves label accuracy.

公开(公告)号：US20210383188A1

公开(公告)日：2021-12-09

申请号：US17072485

申请日：2020-10-16

Applicant: salesforce.com, inc.

Inventor： Pan Zhou ,  Chu Hong Hoi

IPC: G06N3/04 ,  G06N3/08

Abstract: A method for generating a neural network, including initializing the neural network including a plurality of cells, each cell corresponding to a graph including one or more nodes, each node corresponding to a latent representation of a dataset. A plurality of gates are generated, wherein each gate independently determines whether an operation between two nodes is used. A first regularization is performed using the plurality of gates. The first regularization is one of a group-structured sparsity regularization and a path-depth-wised regularization. An optimization is performed on the neural network by adjusting its network parameters and gate parameters based on the regularization of the sparsity.

公开(公告)号：US20220237403A1

公开(公告)日：2022-07-28

申请号：US17161378

申请日：2021-01-28

Applicant: salesforce.com, inc.

Inventor： Pan Zhou ,  Peng Tang ,  Ran Xu ,  Chu Hong Hoi

IPC: G06K9/32 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: A system uses a neural network based model to perform scene text recognition. The system achieves high accuracy of prediction of text from scenes based on a neural network architecture that uses double attention mechanism. The neural network based model includes a convolutional neural network component that outputs a set of visual features and an attention extractor neural network component that determines attention scores based on the visual features. The visual features and the attention scores are combined to generate mixed features that are provided as input to a character recognizer component that determines a second attention score and recognizes the characters based on the second attention score. The system trains the neural network based model by adjusting the neural network parameters to minimize a multi-class gradient harmonizing mechanism (GHM) loss. The multi-class GHM loss varies based on a level of difficulty of the sample.