Analysis of Unsupervised Loss Functions for Homography Estimation

Gadipudi, N. and Elamvazuthi, I. and Lu, C.-K. and Paramasivam, S. and Jegadeeshwaran, R. (2021) Analysis of Unsupervised Loss Functions for Homography Estimation. In: UNSPECIFIED.

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Abstract

Neural networks proved their ability in complex classification and regression problems using labeled data. Recent trends have shown the impressive performance of neural networks in more complex problems like estimating ego-motion and homography tasks. Due to complexity and time consumption for labeling data, researchers tend to exhibit their attentiveness towards unsupervised data-based learning. However, there are no standard loss functions used for image reconstruction and less attention is drawn towards the loss functions than the end to end network architectures. In this paper, we carefully analyze and evaluate the two most commonly used loss functions for the homography estimation task. © 2021 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 0; Conference of 8th International Conference on Intelligent and Advanced Systems, ICIAS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:175661
Uncontrolled Keywords: Complex networks; Machine learning; Network architecture, Homography estimations; Image reconstruction loss; Images reconstruction; Labeled data; Loss functions; Neural-networks; Performance; Recent trends; Regression problem; Unsupervised learning, Image reconstruction
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:30
Last Modified: 10 Nov 2023 03:30
URI: https://khub.utp.edu.my/scholars/id/eprint/15475

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