Using Visualization of Convolutional Neural Networks in Virtual Reality for Machine Learning Newcomers

Meißler, Nadine; Wohlan, Annika; Hochgeschwender, Nico; Schreiber, Andreas

doi:10.1109/aivr46125.2019.00031

Cited by 14 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study [25] and [26] both used VR technology, with the latter also leveraging Unity and Steam VR, for deep learning model development. Their chosen AI system was deep learning neural networks defined using TensorFlow and Keras.…”

Section: A Virtual Reality and Ai Systems (Rq1)mentioning

confidence: 99%

Visualization of AI Systems in Virtual Reality: A Comprehensive Review

Inkarbekov,

Monahan,

Pearlmutter

2023

IJACSA

View full text Add to dashboard Cite

This study provides a comprehensive review of the utilization of Virtual Reality (VR) in the context of Human-Computer Interaction (HCI) for visualizing Artificial Intelligence (AI) systems. Drawing from 18 selected studies, the results illuminate a complex interplay of tools, methods, and approaches, notably the prominence of VR engines like Unreal Engine and Unity. However, despite these tools, a universal solution for effective AI visualization remains elusive, reflecting the unique strengths and limitations of each technique. The application of VR for AI visualization across multiple domains is observed, despite challenges such as high data complexity and cognitive load. Moreover, it briefly discusses the emerging ethical considerations pertaining to the broad integration of these technologies. Despite these challenges, the field shows significant potential, emphasizing the need for dedicated research efforts to unlock the full potential of these immersive technologies. This review, therefore, outlines a roadmap for future research, encouraging innovation in visualization techniques, addressing identified challenges, and considering the ethical implications of VR and AI convergence.

show abstract

Section: A Virtual Reality and Ai Systems (Rq1)mentioning

confidence: 99%

Visualization of AI Systems in Virtual Reality: A Comprehensive Review

Inkarbekov,

Monahan,

Pearlmutter

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…In this context, Meissler et al [39] have developed a technique for visualizing convolutional networks in virtual reality. Their solution is mainly aimed at new designers and aims to provide them with a basic understanding of the functionality of networks.…”

Section: − Collecting Training and Testing Data;mentioning

confidence: 99%

Neural Network Design using a Virtual Reality Platform

Bibbò¹,

Morabito²

2022

GJCST

View full text Add to dashboard Cite

The evolution of Deep Learning (DL), a subset of machine learning, has made their use very effective in many artificial intelligence(AI) fields. In parallel Virtual Reality is going wide in many applications thanks to the proliferation of cameras in mobile devices and improved processing efficiency. Data visualization in deep learning is a fundamental element for which it can benefit from the advantages offered by the visualization of the VR for the development of the models. In addition, the researchers can widely use the editing of images and videos in the machine learning process to design a convolutional network suitable for image recognition. In this study, we want to demonstrate the usefulness of this approach in collecting data within virtual reality to train and optimize a convolutional neural network used to recognize human activities (HAR).

show abstract

“…For example, in patient anatomy applications, the combination of XR and AI can add novelty to the thoracic surgeons' armamentarium by enabling 3D visualization of the complex anatomy of vascular arborization, pulmonary segmental divisions, and bronchial anatomy (Sadeghi et al, 2021). Finally, XR can be used to visualize the deep learning (DL) structure (Meissler et al, 2019;VanHorn et al, 2019…”

Section: Advanced Visualizationmentioning

confidence: 99%

The Combination of Artificial Intelligence and Extended Reality: A Systematic Review

Reiners

Davahli

Karwowski

et al. 2021

Front. Virtual Real.

View full text Add to dashboard Cite

Artificial intelligence (AI) and extended reality (XR) differ in their origin and primary objectives. However, their combination is emerging as a powerful tool for addressing prominent AI and XR challenges and opportunities for cross-development. To investigate the AI-XR combination, we mapped and analyzed published articles through a multi-stage screening strategy. We identified the main applications of the AI-XR combination, including autonomous cars, robotics, military, medical training, cancer diagnosis, entertainment, and gaming applications, advanced visualization methods, smart homes, affective computing, and driver education and training. In addition, we found that the primary motivation for developing the AI-XR applications include 1) training AI, 2) conferring intelligence on XR, and 3) interpreting XR- generated data. Finally, our results highlight the advancements and future perspectives of the AI-XR combination.

show abstract

Using Visualization of Convolutional Neural Networks in Virtual Reality for Machine Learning Newcomers

Cited by 14 publications

References 20 publications

Visualization of AI Systems in Virtual Reality: A Comprehensive Review

Visualization of AI Systems in Virtual Reality: A Comprehensive Review

Neural Network Design using a Virtual Reality Platform

The Combination of Artificial Intelligence and Extended Reality: A Systematic Review

Contact Info

Product

Resources

About