Invention Grant
- Patent Title: Systems for multi-task joint training of neural networks using multi-label datasets
-
Application No.: US17929449Application Date: 2022-09-02
-
Publication No.: US12243292B2Publication Date: 2025-03-04
- Inventor: Shuo Cheng , Wanchun Ma , Linjie Luo
- Applicant: Lemon Inc.
- Applicant Address: KY Grand Cayman
- Assignee: Lemon Inc.
- Current Assignee: Lemon Inc.
- Current Assignee Address: KY Grand Cayman
- Agency: Alleman Hall & Tuttle LLP
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06N3/0455 ; G06N3/09 ; G06V10/44 ; G06V10/764 ; G06V10/766 ; G06V10/774 ; G06V10/776 ; G06V10/778 ; G06V10/82 ; G06V10/96 ; G06V40/16

Abstract:
Systems and methods for multi-task joint training of a neural network including an encoder module and a multi-headed attention mechanism are provided. In one aspect, the system includes a processor configured to receive input data including a first set of labels and a second set of labels. Using the encoder module, features are extracted from the input data. Using a multi-headed attention mechanism, training loss metrics are computed. A first training loss metric is computed using the extracted features and the first set of labels, and a second training loss metric is computed using the extracted features and the second set of labels. A first mask is applied to filter the first training loss metric, and a second mask is applied to filter the second training loss metric. A final training loss metric is computed based on the filtered first and second training loss metrics.
Public/Granted literature
- US20240078792A1 SYSTEMS FOR MULTI-TASK JOINT TRAINING OF NEURAL NETWORKS USING MULTI-LABEL DATASETS Public/Granted day:2024-03-07
Information query