83,063 research outputs found
Аналіз обчислювальних архітектур для реалізації розподіленої САПР
This paper describes a comparative analysis of high-performance computing architectures for building the core infrastructure of a distributed computer-aided design systems, such as a cluster, Grid and Cloud Computing.В данной работе приведены сравнительный анализ высокопроизводительных вычислительных архитектур, таких как вычислительный кластер, Grid, Cloud Computing, для построения базовой информационной инфраструктуры распределенных систем автоматизированного проектирования
Case study of isosurface extraction algorithm performance
Journal ArticleIsosurface extraction is an important and useful visualization method. Over the past ten years, the field has seen numerous isosurface techniques published, leaving the user in a quandary about which one should be used. Some papers have published complexity analysis of the techniques, yet empirical evidence comparing different methods is lacking. This case study presents a comparative study of several representative isosurface extraction algorithms. It reports and analyzes empirical measurements of execution times and memory behavior for each algorithm. The results show that asymptotically optimal techniques may not be the best choice when implemented on modern computer architectures
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
Semantic Segmentation using deep convolutional neural network pose more
complex challenge for any GPU intensive task. As it has to compute million of
parameters, it results to huge memory consumption. Moreover, extracting finer
features and conducting supervised training tends to increase the complexity.
With the introduction of Fully Convolutional Neural Network, which uses finer
strides and utilizes deconvolutional layers for upsampling, it has been a go to
for any image segmentation task. In this paper, we propose two segmentation
architecture which not only needs one-third the parameters to compute but also
gives better accuracy than the similar architectures. The model weights were
transferred from the popular neural net like VGG19 and VGG16 which were trained
on Imagenet classification data-set. Then we transform all the fully connected
layers to convolutional layers and use dilated convolution for decreasing the
parameters. Lastly, we add finer strides and attach four skip architectures
which are element-wise summed with the deconvolutional layers in steps. We
train and test on different sparse and fine data-sets like Pascal VOC2012,
Pascal-Context and NYUDv2 and show how better our model performs in this tasks.
On the other hand our model has a faster inference time and consumes less
memory for training and testing on NVIDIA Pascal GPUs, making it more efficient
and less memory consuming architecture for pixel-wise segmentation.Comment: 8 page
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
The basic features of some of the most versatile and popular open source
frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are
considered and compared. Their comparative analysis was performed and
conclusions were made as to the advantages and disadvantages of these
platforms. The performance tests for the de facto standard MNIST data set were
carried out on H2O framework for deep learning algorithms designed for CPU and
GPU platforms for single-threaded and multithreaded modes of operation Also, we
present the results of testing neural networks architectures on H2O platform
for various activation functions, stopping metrics, and other parameters of
machine learning algorithm. It was demonstrated for the use case of MNIST
database of handwritten digits in single-threaded mode that blind selection of
these parameters can hugely increase (by 2-3 orders) the runtime without the
significant increase of precision. This result can have crucial influence for
optimization of available and new machine learning methods, especially for
image recognition problems.Comment: 15 pages, 11 figures, 4 tables; this paper summarizes the activities
which were started recently and described shortly in the previous conference
presentations arXiv:1706.02248 and arXiv:1707.04940; it is accepted for
Springer book series "Advances in Intelligent Systems and Computing
Towards a performance analysis on pre-trained Visual Question Answering models for autonomous driving
This short paper presents a preliminary analysis of three popular Visual
Question Answering (VQA) models, namely ViLBERT, ViLT, and LXMERT, in the
context of answering questions relating to driving scenarios. The performance
of these models is evaluated by comparing the similarity of responses to
reference answers provided by computer vision experts. Model selection is
predicated on the analysis of transformer utilization in multimodal
architectures. The results indicate that models incorporating cross-modal
attention and late fusion techniques exhibit promising potential for generating
improved answers within a driving perspective. This initial analysis serves as
a launchpad for a forthcoming comprehensive comparative study involving nine
VQA models and sets the scene for further investigations into the effectiveness
of VQA model queries in self-driving scenarios. Supplementary material is
available at
https://github.com/KaavyaRekanar/Towards-a-performance-analysis-on-pre-trained-VQA-models-for-autonomous-driving
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