Results 1 to 10 of about 220,602 (307)
Unsupervised Learning of Depth and Ego-Motion from Video
, 2017 We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. We achieve this by simultaneously training depth and camera pose estimation networks using the task of view ...
Brown, Matthew +3 more
core +1 more source
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
, 2017 Advances in image processing and computer vision in the latest years have brought about the use of visual features in artwork recommendation. Recent works have shown that visual features obtained from pre-trained deep neural networks (DNNs) perform very ...
Dominguez, Vicente +5 more
core +1 more source
Approach or avoidance? Relationship between perceived AI explainability and employee job crafting
Acta PsychologicaAmid growing concerns about the lack of transparency in algorithms, heightened focus has been placed on artificial intelligence (AI) explainability in workplace decision-making processes.
Weiwei Huo +4 more
doaj +1 more source
A New Interpretable Unsupervised Anomaly Detection Method Based on Residual Explanation
IEEE Access, 2022 Despite the superior performance in modeling complex patterns to address challenging problems, the black-box nature of Deep Learning (DL) methods impose limitations to their application in real-world critical domains.
David F. N. Oliveira +8 more
doaj +1 more source
Dependable modulation classifier explainer with measurable explainability
Frontiers in Big Data, 2023 The Internet of Things (IoT) plays a significant role in building smart cities worldwide. Smart cities use IoT devices to collect and analyze data to provide better services and solutions. These IoT devices are heavily dependent on the network for communication.
Gaurav Duggal +2 more
openaire +3 more sources
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceArtificial Intelligence (AI) explainability plays a crucial role in fostering robust Human-AI Interaction (HAI). However, circular reasoning compromises decision robustness due to limitations in existing AI explainability methods.
Changhyun Lee +2 more
doaj +1 more source
International Journal of Strategic Property Management, 2021 This study aims to bridge the gap between two perspectives of explainability−machine learning and engineering, and economics and standard econometrics−by applying three marginal measurements.
Dong-sup Kim, Seungwoo Shin
doaj +1 more source
PLOS Digital Health, 2022 Explainability for artificial intelligence (AI) in medicine is a hotly debated topic. Our paper presents a review of the key arguments in favor and against explainability for AI-powered Clinical Decision Support System (CDSS) applied to a concrete use ...
Julia Amann +13 more
doaj
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability [PDF]
, 2021 R. V. Levin +5 more
openalex +1 more source
Explainable analysis of infrared and visible light image fusion based on deep learning
Scientific ReportsExplainability is a very active area of research in machine learning and image processing. This paper aims to investigate the explainability of visible light and infrared image fusion technology in order to enhance the credibility of model understanding ...
Bo Yuan +4 more
doaj +1 more source