公开(公告)号：US20170228645A1

公开(公告)日：2017-08-10

申请号：US15423360

申请日：2017-02-02

Applicant: NEC Laboratories America, Inc.

Inventor： Linnan WANG ,  Yi YANG ,  Renqiang MIN ,  Srimat CHAKRADHAR

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/084

Abstract: Aspects of the present disclosure describe techniques for training a convolutional neural network using an inconsistent stochastic gradient descent (ISGD) algorithm. Training effort for training batches used by the ISGD algorithm are dynamically adjusted according to a determined loss for a given training batch which are classified into two sub states—well-trained or under-trained. The ISGD algorithm provides more iterations for under-trained batches while reducing iterations for well-trained ones.

公开(公告)号：US20220327814A1

公开(公告)日：2022-10-13

申请号：US17715901

申请日：2022-04-07

Applicant: NEC Laboratories America, Inc.

Inventor： Shaobo HAN ,  Renqiang MIN ,  Tingfeng LI

IPC: G06V10/778 ,  G06V10/82

Abstract: A reinforcement learning based approach to the problem of query object localization, where an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal formulated using the exemplary set by ordinal metric learning. It enables test-time policy adaptation to new environments where the reward signals are not readily available, and thus outperforms fine-tuning approaches that are limited to annotated images. In addition, the transferable reward allows repurposing of the trained agent for new tasks, such as annotation refinement, or selective localization from multiple common objects across a set of images. Experiments on corrupted MNIST dataset and CU-Birds dataset demonstrate the effectiveness of our approach