Sökning: "AlexNet"

Hittade 4 uppsatser innehållade ordet AlexNet.

  1. 1. A deep learning model for scene recognition

    Kandidat-uppsats, Mittuniversitetet/Institutionen för informationssystem och –teknologi

    Författare :Zhaoxin Meng; [2019]
    Nyckelord :Scene recognition; CNN; convolutional supervised; Fisher Vector; transfer learning;

    Sammanfattning : Scene recognition is a hot research topic in the field of image recognition. It is necessary that we focus on the research on scene recognition, because it is helpful to the scene understanding topic, and can provide important contextual information for object recognition. LÄS MER

  2. 2. An Investigation of Low-Rank Decomposition for Increasing Inference Speed in Deep Neural Networks With Limited Training Data

    Uppsats för yrkesexamina på avancerad nivå, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Victor Wikén; [2018]
    Nyckelord :deep neural networks; convolutional neural networks; AlexNet; inference speed; optimization; low-rank tensor decomposition; fine-grained classification problem; dog breed classification; transfer learning;

    Sammanfattning : In this study, to increase inference speed of convolutional neural networks, the optimization technique low-rank tensor decomposition has been implemented and applied to AlexNet which had been trained to classify dog breeds. Due to a small training set, transfer learning was used in order to be able to classify dog breeds. LÄS MER

  3. 3. Deep Learning Algorithms for Cardiac Image Classification and Landmark Detection

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Anton Holm; [2018]
    Nyckelord :Deep Learning; Machine Learning; Medical Image analysis; Image analysis; Deep Neural Networks; Mathematics and Statistics;

    Sammanfattning : With the increase in computational power, deep learning algorithms have become an active field of research over the last 7 years. These data-driven machine learning algorithms have produced good results in many applications, image analysis being one of them. LÄS MER

  4. 4. Terrain Classification to find Drivable Surfaces using Deep Neural Networks : Semantic segmentation for unstructured roads combined with the use of Gabor filters to determine drivable regions trained on a small dataset

    Master-uppsats, KTH/Robotik, perception och lärande, RPL

    Författare :Agneev Guin; [2018]
    Nyckelord :Semantic segmentation; Deep learning; Gabor filters; Drivable surfaces;

    Sammanfattning : Autonomous vehicles face various challenges under difficult terrain conditions such as marginally rural or back-country roads, due to the lack of lane information, road signs or traffic signals. In this thesis, we investigate a novel approach of using Deep Neural Networks (DNNs) to classify off-road surfaces into the types of terrains with the aim of supporting autonomous navigation in unstructured environments. LÄS MER