Self-Supervised Learning for Autonomous Vehicles Perception: A Conciliation Between Analytical and Learning Methods

Chiaroni, Florent; Rahal, Mohamed-Cherif; Hueber, Nicolas; Dufaux, Frédéric

doi:10.1109/msp.2020.2977269

“…Further, the challenges in provisioning understandable explanations and their prevalence with respect to IoT applications were explored. In [161], the authors inves-This article has been accepted for publication in IEEE Open Journal of the Communications Society. This is the author's version which has not been fully edited and content may change prior to final publication.…”

Section: A Edge Xai Structuresmentioning

confidence: 99%

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Jagatheesaperumal¹,

Pham

²

,

Ruby

³

et al. 2022

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

show abstract

“…Further, the challenges in provisioning understandable explanations and their prevalence with respect to IoT applications were explored. In [160], the authors inves-tigated self-supervised learning techniques for the perception of autonomous vehicles. Here, the focus was on the analytical methods and the handcrafted designs, which help to represent appropriate scenes in the perceived environment.…”

Section: A Edge Xai Structuresmentioning

confidence: 99%

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Jagatheesaperumal,

Pham,

Ruby

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.

show abstract

“…Denoted as a branch of semi-supervised learning, SSL has been used in various image-related tasks [39][40][41]. For instance, a broad span of domain adaptation applications [35,41] leverage SSL to learn the decision boundary between source and target data.…”

Section: Self-supervised Learningmentioning

confidence: 99%

Unsupervised Domain Adaption for High-Resolution Coastal Land Cover Mapping with Category-Space Constrained Adversarial Network

Chen

¹

,

Zhai

²

,

Chen

³

et al. 2021

Remote Sensing

4

0

View full text Add to dashboard Cite

Coastal land cover mapping (CLCM) across image domains presents a fundamental and challenging segmentation task. Although adversaries-based domain adaptation methods have been proposed to address this issue, they always implement distribution alignment via a global discriminator while ignoring the data structure. Additionally, the low inter-class variances and intricate spatial details of coastal objects may entail poor presentation. Therefore, this paper proposes a category-space constrained adversarial method to execute category-level adaptive CLCM. Focusing on the underlying category information, we introduce a category-level adversarial framework to align semantic features. We summarize two diverse strategies to extract category-wise domain labels for source and target domains, where the latter is driven by self-supervised learning. Meanwhile, we generalize the lightweight adaptation module to multiple levels across a robust baseline, aiming to fine-tune the features at different spatial scales. Furthermore, the self-supervised learning approach is also leveraged as an improvement strategy to optimize the result within segmented training. We examine our method on two converse adaptation tasks and compare them with other state-of-the-art models. The overall visualization results and evaluation metrics demonstrate that the proposed method achieves excellent performance in the domain adaptation CLCM with high-resolution remotely sensed images.

show abstract

Self-Supervised Learning for Autonomous Vehicles Perception: A Conciliation Between Analytical and Learning Methods

Cited by 18 publications

References 56 publications

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Unsupervised Domain Adaption for High-Resolution Coastal Land Cover Mapping with Category-Space Constrained Adversarial Network

Contact Info

Product

Resources

About