27 research outputs found
Comparative Study of Deep Learning Software Frameworks
Deep learning methods have resulted in significant performance improvements
in several application domains and as such several software frameworks have
been developed to facilitate their implementation. This paper presents a
comparative study of five deep learning frameworks, namely Caffe, Neon,
TensorFlow, Theano, and Torch, on three aspects: extensibility, hardware
utilization, and speed. The study is performed on several types of deep
learning architectures and we evaluate the performance of the above frameworks
when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia
Titan X) settings. The speed performance metrics used here include the gradient
computation time, which is important during the training phase of deep
networks, and the forward time, which is important from the deployment
perspective of trained networks. For convolutional networks, we also report how
each of these frameworks support various convolutional algorithms and their
corresponding performance. From our experiments, we observe that Theano and
Torch are the most easily extensible frameworks. We observe that Torch is best
suited for any deep architecture on CPU, followed by Theano. It also achieves
the best performance on the GPU for large convolutional and fully connected
networks, followed closely by Neon. Theano achieves the best performance on GPU
for training and deployment of LSTM networks. Caffe is the easiest for
evaluating the performance of standard deep architectures. Finally, TensorFlow
is a very flexible framework, similar to Theano, but its performance is
currently not competitive compared to the other studied frameworks.Comment: Submitted to KDD 2016 with TensorFlow results added. At the time of
submission to KDD, TensorFlow was available only with cuDNN v.2 and thus its
performance is reported with that versio
nuts-flow/ml: data pre-processing for deep learning
Data preprocessing is a fundamental part of any machine learning application
and frequently the most time-consuming aspect when developing a machine
learning solution. Preprocessing for deep learning is characterized by
pipelines that lazily load data and perform data transformation, augmentation,
batching and logging. Many of these functions are common across applications
but require different arrangements for training, testing or inference. Here we
introduce a novel software framework named nuts-flow/ml that encapsulates
common preprocessing operations as components, which can be flexibly arranged
to rapidly construct efficient preprocessing pipelines for deep learning.Comment: 11 pages, 4 figure
Optimum Selection of DNN Model and Framework for Edge Inference
This paper describes a methodology to select the optimum combination of deep neuralnetwork and software framework for visual inference on embedded systems. As a first step, benchmarkingis required. In particular, we have benchmarked six popular network models running on four deep learningframeworks implemented on a low-cost embedded platform. Three key performance metrics have beenmeasured and compared with the resulting 24 combinations: accuracy, throughput, and power consumption.Then, application-level specifications come into play. We propose a figure of merit enabling the evaluationof each network/framework pair in terms of relative importance of the aforementioned metrics for a targetedapplication. We prove through numerical analysis and meaningful graphical representations that only areduced subset of the combinations must actually be considered for real deployment. Our approach can beextended to other networks, frameworks, and performance parameters, thus supporting system-level designdecisions in the ever-changing ecosystem of embedded deep learning technology.Ministerio de Economía y Competitividad (TEC2015-66878-C3-1-R)Junta de Andalucía (TIC 2338-2013)European Union Horizon 2020 (Grant 765866
A Survey on Deep Learning Methods for Robot Vision
Deep learning has allowed a paradigm shift in pattern recognition, from using
hand-crafted features together with statistical classifiers to using
general-purpose learning procedures for learning data-driven representations,
features, and classifiers together. The application of this new paradigm has
been particularly successful in computer vision, in which the development of
deep learning methods for vision applications has become a hot research topic.
Given that deep learning has already attracted the attention of the robot
vision community, the main purpose of this survey is to address the use of deep
learning in robot vision. To achieve this, a comprehensive overview of deep
learning and its usage in computer vision is given, that includes a description
of the most frequently used neural models and their main application areas.
Then, the standard methodology and tools used for designing deep-learning based
vision systems are presented. Afterwards, a review of the principal work using
deep learning in robot vision is presented, as well as current and future
trends related to the use of deep learning in robotics. This survey is intended
to be a guide for the developers of robot vision systems
Automated dataset generation for image recognition using the example of taxonomy
This master thesis addresses the subject of automatically generating a
dataset for image recognition, which takes a lot of time when being done
manually. As the thesis was written with motivation from the context of the
biodiversity workgroup at the City University of Applied Sciences Bremen, the
classification of taxonomic entries was chosen as an exemplary use case. In
order to automate the dataset creation, a prototype was conceptualized and
implemented after working out knowledge basics and analyzing requirements for
it. It makes use of an pre-trained abstract artificial intelligence which is
able to sort out images that do not contain the desired content. Subsequent to
the implementation and the automated dataset creation resulting from it, an
evaluation was performed. Other, manually collected datasets were compared to
the one the prototype produced in means of specifications and accuracy. The
results were more than satisfactory and showed that automatically generating a
dataset for image recognition is not only possible, but also might be a decent
alternative to spending time and money in doing this task manually. At the very
end of this work, an idea of how to use the principle of employing abstract
artificial intelligences for step-by-step classification of deeper taxonomic
layers in a productive system is presented and discussed
LSTM Benchmarks for Deep Learning Frameworks
This study provides benchmarks for different implementations of LSTM units
between the deep learning frameworks PyTorch, TensorFlow, Lasagne and Keras.
The comparison includes cuDNN LSTMs, fused LSTM variants and less optimized,
but more flexible LSTM implementations. The benchmarks reflect two typical
scenarios for automatic speech recognition, notably continuous speech
recognition and isolated digit recognition. These scenarios cover input
sequences of fixed and variable length as well as the loss functions CTC and
cross entropy. Additionally, a comparison between four different PyTorch
versions is included. The code is available online
https://github.com/stefbraun/rnn_benchmarks.Comment: 7 pages, 8 figures, 3 table
Deep Learning in Mining Biological Data
Recent technological advancements in data acquisition tools allowed life
scientists to acquire multimodal data from different biological application
domains. Broadly categorized in three types (i.e., sequences, images, and
signals), these data are huge in amount and complex in nature. Mining such an
enormous amount of data for pattern recognition is a big challenge and requires
sophisticated data-intensive machine learning techniques. Artificial neural
network-based learning systems are well known for their pattern recognition
capabilities and lately their deep architectures - known as deep learning (DL)
- have been successfully applied to solve many complex pattern recognition
problems. Highlighting the role of DL in recognizing patterns in biological
data, this article provides - applications of DL to biological sequences,
images, and signals data; overview of open access sources of these data;
description of open source DL tools applicable on these data; and comparison of
these tools from qualitative and quantitative perspectives. At the end, it
outlines some open research challenges in mining biological data and puts
forward a number of possible future perspectives.Comment: 36 pages, 8 figures, and 6 table
Bringing Impressionism to Life with Neural Style Transfer in Come Swim
Neural Style Transfer is a striking, recently-developed technique that uses
neural networks to artistically redraw an image in the style of a source style
image. This paper explores the use of this technique in a production setting,
applying Neural Style Transfer to redraw key scenes in 'Come Swim' in the style
of the impressionistic painting that inspired the film. We document how the
technique can be driven within the framework of an iterative creative process
to achieve a desired look, and propose a mapping of the broad parameter space
to a key set of creative controls. We hope that this mapping can provide
insights into priorities for future research.Comment: 3 pages, 6 figures, paper is a case study of how Neural Style
Transfer can be used in a movie production contex
Neural networks for the Recognition of X-ray Images of Ailments for Covid-19
This investigation analyzes the current state of neural networks, considers the available types, optimizers used for training, describes their benefits and disadvantages. The task of computer vision is defined and the answer to the question why the use of neural networks is an important task today is given. The powerful neural network from Google was proposed as an example and its algorithm is described in detail. Studies have shown how to configure models to get high performance
Monitoring the waste to energy plant using the latest AI methods and tools
Solid wastes for instance, municipal and industrial wastes present great environmental concerns and challenges all over the world. This has led to development of innovative waste-to-energy process technologies capable of handling different waste materials in a more sustainable and energy efficient manner. However, like in many other complex industrial process operations, waste-to-energy plants would require sophisticated process monitoring systems in order to realize very high overall plant efficiencies. Conventional data-driven statistical methods which include principal component analysis, partial least squares, multivariable linear regression and so forth, are normally applied in process monitoring. But recently, latest artificial intelligence (AI) methods in particular deep learning algorithms have demostrated remarkable performances in several important areas such as machine vision, natural language processing and pattern recognition. The new AI algorithms have gained increasing attention from the process industrial applications for instance in areas such as predictive product quality control and machine health monitoring. Moreover, the availability of big-data processing tools and cloud computing technologies further support the use of deep learning based algorithms for process monitoring.
In this work, a process monitoring scheme based on the state-of-the-art artificial intelligence methods and cloud computing platforms is proposed for a waste-to-energy industrial use case. The monitoring scheme supports use of latest AI methods, laveraging big-data processing tools and taking advantage of available cloud computing platforms. Deep learning algorithms are able to describe non-linear, dynamic and high demensionality systems better than most conventional data-based process monitoring methods. Moreover, deep learning based methods are best suited for big-data analytics unlike traditional statistical machine learning methods which are less efficient.
Furthermore, the proposed monitoring scheme emphasizes real-time process monitoring in addition to offline data analysis. To achieve this the monitoring scheme proposes use of big-data analytics software frameworks and tools such as Microsoft Azure stream analytics, Apache storm, Apache Spark, Hadoop and many others. The availability of open source in addition to proprietary cloud computing platforms, AI and big-data software tools, all support the realization of the proposed monitoring scheme