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ETRI Unveils Ultra-Fast Generative Visual Intelligence Model: Creates Images in Just 2 Seconds

ETRI’s researchers have unveiled a technology that combines generative AI and visual intelligence to create images from text inputs in just 2 seconds, propell

South-korea , Korea , Lee-yong-ju , Lee-yong-jae , Telecommunications-research-institute , University-of-wisconsin , Institute-for-information-communications-technology-planning-evaluation , Ministry-of-science , Intelligence-research-section , Stable-diffusion-model , Stable-diffusion , Knowledge-distillation

The Future of ML in Edge Equipment - Circuit Cellar

Internet of Things (IoT) has fueled increased demand for devices with greater intelligence, specifically machine learning (ML), at the network edge.

Brian-santo , Tinyml-foundation , Youtube , Writeratmouser-electronics , Machine-learning-eases-power-requirements , Machine-learning , Knowledge-distillation , Model-optimization , Tensorflow-lite-micro , Edge-impulse , Electronic-news ,

5G & Edge Computing Products and Solutions by Atlantik Elektronik

Atlantik Elektronik offers products and solutions designed for 5G and edge computing and can help you seize the opportunities and overcome the challenges that these technologies bring.

Jesper-rasmussen , Atlantik-elektronik , Qualcomm , Advantages-of-qualcomm-snapdragon-technology , Atlantik-partner-network , Edge-computing , Portfolio-overview , Qualcomm-snapdragon , Quantization-aware-training , Knowledge-distillation , Elektronik-offerings ,

"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 assists the learning of a very compact student and significantly reduces the training time. We also introduce Bio-HSI, a new large benchmark hyperspectral image dataset of 3,125 high-resolution images with dense segmentation ground truth. This new, large dataset can be expected to advance research on deep models for hyperspectral image segmentation. Evaluated on this dataset, the student trained via our KDM has 762 times fewer parameters than the state-of-the-art segmentation model (i.e., HRNet), while achieving competitive accuracy.

Knowledge-distillation , Multi-head-teacher , Deep-learning , Yperspectral-image-segmentation , Nowledge-distillation ,

Deep learning model compression

Deep learning model compression
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Cho-hariharan , Facebook-research , Towards-understanding-ensemble , Knowledge-distillation , Deep-learning , Read-students-learn-better , Pre-training-compact-models , Pre-trained-distillation , முகநூல்-ஆராய்ச்சி , அறிவு-வடித்தல் , ஆழமான-கற்றல்