2022-11-28-cheng22a.md

title	abstract	video	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Trustworthiness Evaluation and Trust-Aware Design of CNN Architectures	Convolutional neural networks (CNNs) are known to be effective tools in many deep learning application areas. Despite CNN’s good performance in terms of classical evaluation metrics such as accuracy and loss, quantifying and ensuring a high degree of trustworthiness of such models remains an unsolved problem raising questions in applications where trust is an important factor. In this work, we propose a framework to evaluate the trustworthiness of CNNs. Towards this end, we develop a trust-based pooling layer for CNNs to achieve higher accuracy and trustworthiness in applications with noise in input features. We further propose TrustCNets consisting of trustworthiness-aware CNN building blocks, i.e., one or more conv layers followed by a trust-based pooling layer. TrustCNets can stack together as a trust-aware CNN architecture or be plugged into deep learning architectures to improve performance. In our experiments, we evaluate the trustworthiness of popular CNN building blocks and demonstrate the performance of our TrustCNet empirically with multiple datasets.	https://youtu.be/wnG46KDvsPg	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	cheng22a	0	Trustworthiness Evaluation and Trust-Aware Design of CNN Architectures	1086	1102	1086-1102	1086	false	Cheng, Mingxi and Sun, Tingyang and Nazarian, Shahin and Bogdan, Paul	given family Mingxi Cheng given family Tingyang Sun given family Shahin Nazarian given family Paul Bogdan	2022-11-28		Proceedings of The 1st Conference on Lifelong Learning Agents	199	inproceedings	date-parts 2022 11 28	https://proceedings.mlr.press/v199/cheng22a/cheng22a.pdf