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Implicit Regularization of a Deep Augmented Neural Network Model for Human Motion Prediction by Gaurav Kumar Yadav; Mohamed Abdel-Nasser; Hatem A. Rashwan; Domenec Puig; G. C. Nandi is a Computer Science article available to read on EtoBox.

What is Implicit Regularization of a Deep Augmented Neural Network Model for Human Motion Prediction about?

Predicting human motion based on past observed motion is one of the challenging issues in computer vision and graphics. Existing research works are dealing with this issue by using discriminative models and showing the results for cases that follow a homogeneous distribution (in distribution) and not discussing the issues of the domain shift problem, where training and testing data follow a heterogeneous (out of distribution) problem, which is the reality when such models are used in practice. However, recent research proposed addressing domain shift issues by augmenting the discriminative model with a generative model and obtained better results. In the present investigation, we propose regularizing the extended network by inserting linear layers to minimize the rank of the latent space and train the entire end-to-end network. We regularize the network to strengthen the model to deal effectively with domain shift scenarios. Both training and testing data come from different distribution sets; to deal with this, we toughen our network by adding the extra linear layers to the network encoder. We tested our model with the benchmark datasets, CMU Motion Capture and Human3.6M, and prov

Who reads Implicit Regularization of a Deep Augmented Neural Network Model for Human Motion Prediction?

Author

Gaurav Kumar Yadav; Mohamed Abdel-Nasser; Hatem A. Rashwan; Domenec Puig; G. C. Nandi

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)