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pro vyhledávání: '"Kamakshi, Vidhya"'
Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domain is fundamental to domain adaptation. While these techniques result i
Externí odkaz:
http://arxiv.org/abs/2205.09943
Deep CNNs, though have achieved the state of the art performance in image classification tasks, remain a black-box to a human using them. There is a growing interest in explaining the working of these deep models to improve their trustworthiness. In
Externí odkaz:
http://arxiv.org/abs/2108.13828
A particular class of Explainable AI (XAI) methods provide saliency maps to highlight part of the image a Convolutional Neural Network (CNN) model looks at to classify the image as a way to explain its working. These methods provide an intuitive way
Externí odkaz:
http://arxiv.org/abs/2106.12773
The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The human interpretable rule is defined as an axis-aligned hyper-cuboid containing the instance for which the classificat
Externí odkaz:
http://arxiv.org/abs/2011.01506
Deep convolutional networks have been quite successful at various image classification tasks. The current methods to explain the predictions of a pre-trained model rely on gradient information, often resulting in saliency maps that focus on the foreg
Externí odkaz:
http://arxiv.org/abs/2011.01472
Autor:
Kamakshi, Vidhya1 (AUTHOR) 2017csz0005@iitrpr.ac.in, Krishnan, Narayanan C.1,2 (AUTHOR) ckn@iitpkd.ac.in
Publikováno v:
AI. Sep2023, Vol. 4 Issue 3, p620-651. 32p.
