Wyniki wyszukiwania dla: SIGN LANGUAGE, CONVOLUTIONAL NEURAL NETWORK (CNN), QUANTIZATION AWARE TRAINING (QAT), LAYER DECOMPOSITION, KNOWLEDGE DISTILLATION - MOST Wiedzy

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Wyniki wyszukiwania dla: SIGN LANGUAGE, CONVOLUTIONAL NEURAL NETWORK (CNN), QUANTIZATION AWARE TRAINING (QAT), LAYER DECOMPOSITION, KNOWLEDGE DISTILLATION

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Wyniki wyszukiwania dla: SIGN LANGUAGE, CONVOLUTIONAL NEURAL NETWORK (CNN), QUANTIZATION AWARE TRAINING (QAT), LAYER DECOMPOSITION, KNOWLEDGE DISTILLATION

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Wyniki wyszukiwania dla: SIGN LANGUAGE, CONVOLUTIONAL NEURAL NETWORK (CNN), QUANTIZATION AWARE TRAINING (QAT), LAYER DECOMPOSITION, KNOWLEDGE DISTILLATION

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Wyniki wyszukiwania dla: SIGN LANGUAGE, CONVOLUTIONAL NEURAL NETWORK (CNN), QUANTIZATION AWARE TRAINING (QAT), LAYER DECOMPOSITION, KNOWLEDGE DISTILLATION

  • Sign Language Recognition Using Convolution Neural Networks

    Publikacja

    - Rok 2024

    The objective of this work was to provide an app that can automatically recognize hand gestures from the American Sign Language (ASL) on mobile devices. The app employs a model based on Convolutional Neural Network (CNN) for gesture classification. Various CNN architectures and optimization strategies suitable for devices with limited resources were examined. InceptionV3 and VGG-19 models exhibited negligibly higher accuracy than...

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  • Vehicle detector training with labels derived from background subtraction algorithms in video surveillance

    Publikacja

    - Rok 2018

    Vehicle detection in video from a miniature station- ary closed-circuit television (CCTV) camera is discussed in the paper. The camera provides one of components of the intelligent road sign developed in the project concerning the traffic control with the use of autonomous devices being developed. Modern Convolutional Neural Network (CNN) based detectors need big data input, usually demanding their manual labeling. In the presented...

  • Speaker Recognition Using Convolutional Neural Network with Minimal Training Data for Smart Home Solutions

    Publikacja

    - Rok 2018

    With the technology advancements in smart home sector, voice control and automation are key components that can make a real difference in people's lives. The voice recognition technology market continues to involve rapidly as almost all smart home devices are providing speaker recognition capability today. However, most of them provide cloud-based solutions or use very deep Neural Networks for speaker recognition task, which are...

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  • Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters

    Publikacja

    - Rok 2019

    This paper focuses on convolution neural network quantization problem. The quantization has a distinct stage of data conversion from floating-point into integer-point numbers. In general, the process of quantization is associated with the reduction of the matrix dimension via limited precision of the numbers. However, the training and inference stages of deep learning neural network are limited by the space of the memory and a...

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  • Resource constrained neural network training

    Publikacja

    - Scientific Reports - Rok 2024

    Modern applications of neural-network-based AI solutions tend to move from datacenter backends to low-power edge devices. Environmental, computational, and power constraints are inevitable consequences of such a shift. Limiting the bit count of neural network parameters proved to be a valid technique for speeding up and increasing efficiency of the inference process. Hence, it is understandable that a similar approach is gaining...

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