MyJournals Home  

RSS FeedsAlgorithms, Vol. 11, Pages 157: LSTM Accelerator for Convolutional Object Identification (Algorithms)

 
 

17 october 2018 12:00:16

 
Algorithms, Vol. 11, Pages 157: LSTM Accelerator for Convolutional Object Identification (Algorithms)
 


Deep Learning has dramatically advanced the state of the art in vision, speech and many other areas. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this paper, in order to detect the version that can provide the best trade-off in terms of time and accuracy, convolutional networks of various depths have been implemented. Batch normalization is also considered since it acts as a regularizer and achieves the same accuracy with fewer training steps. For maximizing the yield of the complexity by diminishing, as well as minimizing the loss of accuracy, LSTM neural net layers are utilized in the process. The image sequences are proven to be classified by the LSTM in a more accelerated manner, while managing better precision. Concretely, the more complex the CNN, the higher the percentages of exactitude; in addition, but for the high-rank increase in accuracy, the time was significantly decreased, which eventually rendered the trade-off optimal. The average improvement of performance for all models regarding both datasets used amounted to 42 % .


 
72 viewsCategory: Informatics
 
Algorithms, Vol. 11, Pages 152: Accelerated Iterative Learning Control of Speed Ripple Suppression for a Seeker Servo Motor (Algorithms)
Algorithms, Vol. 11, Pages 156: Online Uniformly Inserting Points on the Sphere (Algorithms)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Informatics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten