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Your search keyword '"Lee, Ivan"' showing total 4 results
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4 results on '"Lee, Ivan"'

3. On-Device Saliency Prediction Based on Pseudoknowledge Distillation.

Author

Umer, Ayaz, Termritthikun, Chakkrit, Qiu, Tie, Leong, Philip H. W., and Lee, Ivan

Abstract

Saliency prediction models aim to mimic the human visual system’s attention process, and the research has made significant progress due to recent advancements in deep convolution neural networks. However, the high memory requirements and intensive computational demands make these approaches less suitable for Internet-of-Things (IoT) devices, and there is a need for an improved computational efficiency and reduced memory footprint to facilitate distributed IoT intelligence. This article proposes a pseudoknowledge distillation (PKD) training method for creating a compact real-time saliency prediction model. The proposed method can effectively transfer knowledge from computationally expensive once-for-all (OFA-595) as a single teacher model and a combination of OFA-595 and EfficientNet-B7 as a multiteacher model to an early exit evolutionary algorithm network student model by utilizing knowledge distillation and pseudolabeling. Five saliency benchmark datasets are used to demonstrate PKD’s improved prediction performance and its reduced inference time without modifying the original student model. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Neural Architecture Search and Multi-Objective Evolutionary Algorithms for Anomaly Detection

Author

Chakkrit Termritthikun, Lin Xu, Yemeng Liu, Ivan Lee, Termritthikun, Chakkrit, Xu, Lin, Liu, Yemeng, Lee, Ivan, and 2021 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 virtual 7-10 December 2021

Subjects
multi-objective, neural architecture search, firefly algorithm, genetic algorithm, deep learning
Abstract

Refereed/Peer-reviewed The processing speed and memory footprint are important factors for applications processing on resource-constrained devices such as IoT devices and embedded systems. Deep learning has been evolving continuously so that it can be used on resource-constrained devices but there are still some limitations in using it because these devices are not designed for processing complicated tasks. Further, the complexity of the Convolutional Neural Network (CNN) model is the barrier to implementation on these devices. In this paper, we have developed Neural Architecture Search (NAS) that uses a Multi-Objective Genetic Algorithm and Firefly Algorithm for creating a less complicated and robust CNN model, focusing on searching the model with faster processing time and minimum storage size. Five image datasets are applied to examine the performance of the proposed techniques, including two crack detection datasets for surface or built infrastructure inspection for industrial applications. Experimental results show that the proposed technique consistently lowers the parameter counts and processing time at comparable classification accuracies.

Published
2021

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