convolutional neural networks

This dataset contains the images used in the paper "Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time". M. E. Morocho Cayamcela and W. Lim, "Fine-tuning a pre-trained Convolutional Neural Network Model to translate American Sign Language in Real-time," 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 2019, pp. 100-104.

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  • Artificial Intelligence
  • Last Updated On: 
    Tue, 02/18/2020 - 11:06

    Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. This dataset contains 388 images of structures with and without dirt buildup.

    One can use this dataset for experimentation on classifying the dirt buildup.

    53 views
  • Artificial Intelligence
  • Last Updated On: 
    Wed, 01/29/2020 - 18:54

    Since there is no image-based personality dataset, we used the ChaLearn dataset for creating a new dataset that met the characteristics we required for this work, i.e., selfie images where only one person appears and his face is visible, labeled with the person's apparent personality in the photo.

    124 views
  • Artificial Intelligence
  • Last Updated On: 
    Thu, 01/02/2020 - 17:47

    As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (~1.72M frames) traffic sign detection video dataset (CURE-TSD) which is among the most comprehensive datasets with controlled synthetic challenging conditions. The video sequences in the 

    372 views
  • Artificial Intelligence
  • Last Updated On: 
    Sun, 10/13/2019 - 17:07

    As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed.

    234 views
  • Artificial Intelligence
  • Last Updated On: 
    Sun, 10/13/2019 - 17:08

     

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  • Power and Energy
  • Last Updated On: 
    Mon, 05/27/2019 - 08:19

    Our goal is to find whether a convolutional neural network (CNN) performs better than the existing blind algorithms for image denoising, and, if yes, whether the noise statistics has an effect on the performance gap. We performed automatic identification of noise distribution, over a set of nine possible distributions, namely, Gaussian, log-normal, uniform, exponential, Poisson, salt and pepper, Rayleigh, speckle and Erlang. Next, for each of these noisy image sets, we compared the performance of FFDNet, a CNN based denoising method, with noise clinic, a blind denoising algorithm.

    664 views
  • Communications
  • Last Updated On: 
    Fri, 12/20/2019 - 05:33

    Device Fingerprinting for Access Control over a Campus and Isolated Network Device Fingerprinting (DFP) is a technique to identify devices using Inter-Arrival Time (IAT) of packets and without using any other unique identifier. Our experiments include generating graphs of IATs of 100 packets and using Convolutional Neural Network on the generated graphs to identify a device. We did two experiments where the first experiment was on Raspberri Pi and other experiment was on crawdad dataset. First Experiment: Raspberry Pi We developed a packet sniffer application to capture IAT of packets.

    100 views
  • Communications
  • Last Updated On: 
    Sat, 02/02/2019 - 22:53

    This file contains all data used on paper "Analyzing and Increasing the Reliability of Convolutional Neural Networks on GPUs"

    271 views
  • Reliability
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34