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"AdaptorNAS: A New Perturbation-based Neural Architecture Search for Hy" by Sui Paul Ang, Son Lam Phung et al.

Hyperspectral image segmentation is an emerging area with numerous applications, including agriculture, forestry, environment monitoring, and remote sensing. This paper proposes a new neural architecture search algorithm, named AdaptorNAS, for hyperspectral image segmentation. AdaptorNAS aims to design the optimum decoder for any given encoder. In our approach, the search space of AdaptorNAS is a large deep neural network (DNN), and the optimal decoder is derived by pruning the large DNN via a perturbation-based pruning strategy. Verified on three popular encoders, i.e., ResNet-34, MobileNet-V2, and EfficientNet-B2, AdaptorNAS can design high-speed decoders that are significantly better than six common hand-crafted decoders. Additionally, with the EfficientNet-B2 encoder, AdaptorNAS (mIoU of 92.47% and mDice of 95.15%) outperforms the state-of-the-art NAS algorithms and hand-crafted network architectures on the hyperspectral image segmentation task. We also introduce a new hyperspectra ....

Biosecurity Scanning , Computer Architecture , Deep Learning , Feature Extraction , Yperspectral Image Segmentation , Hyperspectral Imaging , Image Segmentation , Neural Architecture Search , Erturbation Based Search , Semantic Segmentation ,

"Neural Architecture Search for Image Segmentation and Classification" by Sui Paul Ang

Deep learning (DL) is a class of machine learning algorithms that relies on deep neural networks (DNNs) for computations. Unlike traditional machine learning algorithms, DL can learn from raw data directly and effectively. Hence, DL has been successfully applied to tackle many real-world problems. When applying DL to a given problem, the primary task is designing the optimum DNN. This task relies heavily on human expertise, is time-consuming, and requires many trial-and-error experiments.
This thesis aims to automate the laborious task of designing the optimum DNN by exploring the neural architecture search (NAS) approach. Here, we propose two new NAS algorithms for two real-world problems: pedestrian lane detection for assistive navigation and hyperspectral image segmentation for biosecurity scanning. Additionally, we also introduce a new dataset-agnostic predictor of neural network performance, which can be used to speed-up NAS algorithms that require the evaluation of candidate D ....

Neural Architecture Search , Deep Learning , Yperspectral Image Segmentation , Edestrian Lane Detection , Eural Network Performance Prediction ,

"Efficient hyperspectral image segmentation for biosecurity scanning us" by Minh Hieu Phan, Son Lam Phung et al.

Foreign species can deteriorate the environment and the economy of a country. To automatically monitor biosecurity threats at country borders, this paper investigates compact deep networks for accurate and real-time object segmentation for hyperspectral images. To this end, knowledge distillation (KD) approaches compress the model by distilling the knowledge of a large teacher network to a compact student network. However, when the student is over-compressed, the performance of standard KD methods degrades significantly due to the large capacity gap between the teacher and the student. This gap can be addressed by adding medium-sized teacher assistants, but training them incurs significant computation and hence is impractical. To address this problem, this paper proposes a new framework called Knowledge Distillation from Multi-head Teacher (KDM), which distills the knowledge of a multi-head teacher to the student. By encapsulating multiple teachers in a single network, our proposed KDM ....

Knowledge Distillation , Multi Head Teacher , Deep Learning , Yperspectral Image Segmentation , Knowledge Distillation ,

"Deep Learning for Hyperspectral Image Segmentation in Biosecurity Scan" by Ly Bui

Biosecurity scanning plays a crucial role in preventing exotic pests, weeds and contaminants from entering a country through shipping containers. Exposure to biosecurity risks causes a substantial loss to the native environment, production value and public health. Currently, these threats are managed via manual inspection, detector dogs and x-ray scanners; however, these procedures are time-consuming, error-prone, or costly.
In this research, we propose a novel approach for biosecurity risk detection that utilizes hyperspectral imaging technology and semantic image segmentation. This approach segments the target objects in a hyperspectral image by analyzing their spatial and spectral signatures. The target objects in this project include metal, plants, soil, creatures and background. ....

Deep Learning , Yperspectral Image Segmentation , Biosecurity Scanning ,