Discriminative Dictionary Design for Action Classification in Still Images and Videos

Roy, Aidan; Banerjee, Biplab; Hussain, Amir; Poria, Soujanya

doi:10.1007/s12559-021-09851-8

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Since then, deep reinforcement learning (RL)based programs and applications have been developed to determine the degree to which they can compete with human champion players in various games like Texas hold'em poker [11] and Dota 2 [12]. Taking into consideration the revolutionary success of AI, researchers predict that it will snowball in the next few years, reaching $190.61 billion market value in 2025 [13][14][15]. Consequently, the proliferation of AI has made people think, "How comfortable are we trusting blindly on these AI-generated predictions and results?…”

Section: A Brief Historical Perspectivementioning

confidence: 99%

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Hassija,

Chamola,

Mahapatra

et al. 2023

Cogn Comput

Self Cite

127

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Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models. Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'. One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them. Due to the rapid proleferation of these AI models, explaining their learning and decision making process are getting harder which require transparency and easy predictability. Aiming to collate the current state-of-the-art in interpreting the black-box models, this study provides a comprehensive analysis of the explainable AI (XAI) models. To reduce false negative and false positive outcomes of these back-box models, finding flaws in them is still difficult and inefficient. In this paper, the development of XAI is reviewed meticulously through careful selection and analysis of the current state-of-the-art of XAI research. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers. Towards the end, it highlights emerging and critical issues pertaining to XAI research to showcase major, model-specific trends for better explanation, enhanced transparency, and improved prediction accuracy.

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Section: A Brief Historical Perspectivementioning

confidence: 99%

Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Hassija,

Chamola,

Mahapatra

et al. 2023

Cogn Comput

Self Cite

127

View full text Add to dashboard Cite

show abstract

“…Deep learning has achieved great success in many domains including image recognition [1,2], natural language processing [3,4], constrained learning [5], and intelligent decision [6,7]. On the other hand, deep neural networks are typically of high complexity with ultra-high-dimensional features [8,9,10].…”

Section: Introductionmentioning

confidence: 99%