Knowledge Distillation News Today

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

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

Over the past few years, deep learning (DL) has been achieving state-of-theart performance on various human tasks such as speech generation, language translation, image segmentation, and object detection. While traditional machine learning models require hand-crafted features, deep learning algorithms can automatically extract discriminative features and learn complex knowledge from large datasets. This powerful learning ability makes deep learning models attractive to both academia and big corp...

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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 metho...

Condition monitoring and fault diagnosis have been critical for the optimal scheduling of machines, improving the system reliability and the reducing maintenance cost. In recent years, various of methods based on the deep learning method have made the great progress in the field of the mechanical fault diagnosis. However, there is a conflict between the massive parameters of the fault diagnosis networks and the limited computing resource of the embedded platforms. It is difficult to deploy the t...