公开(公告)号：US11586928B2

公开(公告)日：2023-02-21

申请号：US16265906

申请日：2019-02-01

Applicant: Tata Consultancy Services Limited

Inventor： Tulika Bose ,  Angshul Majumdar ,  Tanushyam Chattopadhyay

IPC: G06N3/04 ,  G06N3/08 ,  H04L45/50

Abstract: A method and system for incorporating regression into a Stacked Auto Encoder utilizing deep learning based regression technique that enables joint learning of parameters for a regression model to train the SAE for a regression problem. The method comprises generating a regression model for the SAE for solving the regression problem, wherein regression model is formulated as a non-convex joint optimization function for an asymmetric SAE. The method further comprises reformulating the non-convex joint optimization function as an Augmented Lagrangian formulation in terms of a plurality of proxy variables and a plurality of hyper parameters. The method comprises splitting the Augmented Lagrangian formulation into sub-problems using Alternating Direction Method of Multipliers and jointly learning parameters for the regression model to train the SAE for the regression problem. The learned weights enable estimating the unknown target values.

公开(公告)号：US10360665B2

公开(公告)日：2019-07-23

申请号：US15898144

申请日：2018-02-15

Applicant: Tata Consultancy Services Limited

Inventor： Kavya Gupta ,  Brojeshwar Bhowmick ,  Angshul Majumdar

IPC: G06T5/00 ,  G06T9/00 ,  G06T1/20 ,  G06N3/04

Abstract: Motion blur occur when acquiring images and videos with cameras fitted to the high speed motion devices, for example, drones. Distorted images intervene with the mapping of the visual points, hence the pose estimation and tracking may get corrupted. System and method for solving inverse problems using a coupled autoencoder is disclosed. In an embodiment, solving inverse problems, for example, generating a clean sample from an unknown corrupted sample is disclosed. The coupled autoencoder learns the autoencoder weights and coupling map (between source and target) simultaneously. The technique is applicable to any transfer learning problem. The embodiments of the present disclosure implements/proposes a new formulation that recasts deblurring as a transfer learning problem which is solved using the proposed coupled autoencoder.

公开(公告)号：US11216692B2

公开(公告)日：2022-01-04

申请号：US16502760

申请日：2019-07-03

Applicant: Tata Consultancy Services Limited

Inventor： Kavya Gupta ,  Brojeshwar Bhowmick ,  Angshul Majumdar

IPC: G06K9/62

Abstract: This disclosure relates to systems and methods for solving generic inverse problems by providing a coupled representation architecture using transform learning. Convention solutions are complex, require long training and testing times, reconstruction quality also may not be suitable for all applications. Furthermore, they preclude application to real-time scenarios due to the mentioned inherent lacunae. The methods provided herein require involve very low computational complexity with a need for only three matrix-vector products, and requires very short training and testing times, which makes it applicable for real-time applications. Unlike the conventional learning architectures using inductive approaches, the CASC of the present disclosure can learn directly from the source domain and the number of features in a source domain may not be necessarily equal to the number of features in a target domain.

公开(公告)号：US10964076B2

公开(公告)日：2021-03-30

申请号：US16504196

申请日：2019-07-05

Applicant: Tata Consultancy Services Limited

Inventor： Jayavardhana Rama Gubbi Lakshminarasimha ,  Karthik Seemakurthy ,  Sandeep Nk ,  Ashley Varghese ,  Shailesh Shankar Deshpande ,  Mariaswamy Girish Chandra ,  Balamuralidhar Purushothaman ,  Angshul Majumdar

IPC: G06T11/00 ,  G06T3/40 ,  G06T7/00

Abstract: This disclosure relates generally to image processing, and more particularly to method and system for image reconstruction using deep dictionary learning (DDL). The system collects the degraded image as test image and processes the test image to extract sparse features from the test image, at different levels, using dictionaries. The extracted sparse features and data from the dictionaries are used by the system to reconstruct the HR image corresponding to the test image.