On Determining Suitable Embedded Devices for Deep Learning Models

Deep learning (DL) networks have proven to be crucial in commercial solutions with computer vision challenges due to their abilities to extract high-level abstractions of the image data and their capabilities of being easily adapted to many applications. As a result, DL methodologies had become a de facto standard for computer vision problems yielding many new kinds of research, approaches and applications. Recently, the commercial sector is also driving to use of embedded systems to be able to execute DL models, which has caused an important change on the DL panorama and the embedded systems themselves. Consequently, in this paper, we attempt to study the state of the art of embedded systems, such as GPUs, FPGAs and Mobile SoCs, that are able to use DL techniques, to modernize the stakeholders with the new systems available in the market. Besides, we aim at helping them to determine which of these systems can be beneficial and suitable for their applications in terms of upgradeability, price, deployment and performance.

Download Full-text

Recovery algorithm to correct silent data corruption of synaptic storage in convolutional neural networks

International Journal of Hybrid Intelligent Systems ◽

10.3233/his-200278 ◽

2020 ◽

Vol 16 (3) ◽

pp. 177-187

Author(s):

Arighna Roy ◽

Simone A. Ludwig

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Embedded Systems ◽

Convolutional Neural Networks ◽

Embedded Devices ◽

Data Set ◽

Recovery Algorithm ◽

Embedded Vision ◽

Data Corruption ◽

High Level

With the surge of computational power and efficient energy consumption management on embedded devices, embedded processing has grown exponentially during the last decade. In particular, computer vision has become prevalent in real-time embedded systems, which have always been a victim of transient fault due to its pervasive presence in harsh environments. Convolutional Neural Networks (CNN) are popular in the domain of embedded vision (computer vision in embedded systems) given the success they have shown. One problem encountered is that a pre-trained CNN on embedded devices is vastly affected by Silent Data Corruption (SDC). SDC refers to undetected data corruption that causes errors in data without any indication that the data is incorrect, and thus goes undetected. In this paper, we propose a software-based approach to recover the corrupted bits of a pre-trained CNN due to SDC. Our approach uses a rule-mining algorithm and we conduct experiments on the propagation of error through the topology of the CNN in order to detect the association of the bits for the weights of the pre-trained CNN. This approach increases the robustness of safety-critical embedded vision applications in volatile conditions. A proof of concept has been conducted for a combination of a CNN and a vision data set. We have successfully established the effectiveness of this approach for a very high level of SDC. The proposed approach can further be extended to other networks and data sets.

Download Full-text

Automating Morphological Profiling with Generic Deep Convolutional Networks

10.1101/085118 ◽

2016 ◽

Cited By ~ 19

Author(s):

Nick Pawlowski ◽

Juan C Caicedo ◽

Shantanu Singh ◽

Anne E Carpenter ◽

Amos Storkey

Keyword(s):

Computer Vision ◽

Feature Extraction ◽

Deep Learning ◽

Processing Speed ◽

State Of The Art ◽

Learning Models ◽

Convolutional Networks ◽

Automated Processing ◽

Classical Computer ◽

Microscopy Images

AbstractMorphological profiling aims to create signatures of genes, chemicals and diseases from microscopy images. Current approaches use classical computer vision-based segmentation and feature extraction. Deep learning models achieve state-of-the-art performance in many computer vision tasks such as classification and segmentation. We propose to transfer activation features of generic deep convolutional networks to extract features for morphological profiling. Our approach surpasses currently used methods in terms of accuracy and processing speed. Furthermore, it enables fully automated processing of microscopy images without need for single cell identification.

Download Full-text

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

10.26434/chemrxiv.12562121 ◽

2020 ◽

Author(s):

Dean Sumner ◽

Jiazhen He ◽

Amol Thakkar ◽

Ola Engkvist ◽

Esben Jannik Bjerrum

Keyword(s):

Neural Networks ◽

Pattern Recognition ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Data Augmentation ◽

State Of The Art ◽

Sequence Similarity ◽

Learning Models ◽

Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

Download Full-text

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

10.31232/osf.io/4pxq2 ◽

2020 ◽

Author(s):

Saeed Nosratabadi ◽

Amir Mosavi ◽

Puhong Duan ◽

Pedram Ghamisi ◽

Ferdinand Filip ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Science ◽

State Of The Art ◽

Science Methods ◽

Learning Models ◽

Diverse Range ◽

Hybrid Machine ◽

Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Download Full-text

U-Infuse: Democratization of Customizable Deep Learning for Object Detection

Sensors ◽

10.3390/s21082611 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2611

Author(s):

Andrew Shepley ◽

Greg Falzon ◽

Christopher Lawson ◽

Paul Meek ◽

Paul Kwan

Keyword(s):

Deep Learning ◽

Intellectual Property ◽

Object Detection ◽

Image Data ◽

Learning Technologies ◽

Training Data ◽

Learning Models ◽

Ecological Data ◽

Single Class ◽

Large Numbers

Image data is one of the primary sources of ecological data used in biodiversity conservation and management worldwide. However, classifying and interpreting large numbers of images is time and resource expensive, particularly in the context of camera trapping. Deep learning models have been used to achieve this task but are often not suited to specific applications due to their inability to generalise to new environments and inconsistent performance. Models need to be developed for specific species cohorts and environments, but the technical skills required to achieve this are a key barrier to the accessibility of this technology to ecologists. Thus, there is a strong need to democratize access to deep learning technologies by providing an easy-to-use software application allowing non-technical users to train custom object detectors. U-Infuse addresses this issue by providing ecologists with the ability to train customised models using publicly available images and/or their own images without specific technical expertise. Auto-annotation and annotation editing functionalities minimize the constraints of manually annotating and pre-processing large numbers of images. U-Infuse is a free and open-source software solution that supports both multiclass and single class training and object detection, allowing ecologists to access deep learning technologies usually only available to computer scientists, on their own device, customised for their application, without sharing intellectual property or sensitive data. It provides ecological practitioners with the ability to (i) easily achieve object detection within a user-friendly GUI, generating a species distribution report, and other useful statistics, (ii) custom train deep learning models using publicly available and custom training data, (iii) achieve supervised auto-annotation of images for further training, with the benefit of editing annotations to ensure quality datasets. Broad adoption of U-Infuse by ecological practitioners will improve ecological image analysis and processing by allowing significantly more image data to be processed with minimal expenditure of time and resources, particularly for camera trap images. Ease of training and use of transfer learning means domain-specific models can be trained rapidly, and frequently updated without the need for computer science expertise, or data sharing, protecting intellectual property and privacy.

Download Full-text

InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

BMC Bioinformatics ◽

10.1186/s12859-021-04037-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Dominik Jens Elias Waibel ◽

Sayedali Shetab Boushehri ◽

Carsten Marr

Keyword(s):

Image Processing ◽

Deep Learning ◽

Specific Problem ◽

State Of The Art ◽

Image Data ◽

Semantic Segmentation ◽

Parameter Tuning ◽

Cellular Processes ◽

Minimal Effort ◽

Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

Download Full-text

Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning

10.36227/techrxiv.14762043.v1 ◽

2021 ◽

Author(s):

Phongsathorn Kittiworapanya ◽

Kitsuchart Pasupa ◽

Peter Auer

Keyword(s):

Computer Vision ◽

Particle Size ◽

Deep Learning ◽

Waste Management ◽

State Of The Art ◽

Learning Algorithms ◽

Input Image ◽

Size Estimation ◽

Waste Particles ◽

Set Up

<div>We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.<br></div>

Download Full-text

A Survey of Graphical Page Object Detection with Deep Neural Networks

10.20944/preprints202104.0739.v1 ◽

2021 ◽

Author(s):

Jwalin Bhatt ◽

Khurram Azeem Hashmi ◽

Muhammad Zeshan Afzal ◽

Didier Stricker

Keyword(s):

Deep Learning ◽

Object Detection ◽

Conceptual Understanding ◽

Deep Neural Networks ◽

State Of The Art ◽

Learning Approaches ◽

Document Images ◽

Essential Information ◽

Current State ◽

High Level

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.

Download Full-text

Covid-19 detection via deep neural network and occlusion sensitivity maps

10.36227/techrxiv.14100890 ◽

2021 ◽

Author(s):

Noor Ahmad ◽

Muhammad Aminu ◽

Mohd Halim Mohd Noor

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

State Of The Art ◽

Color Images ◽

Fine Tuning ◽

Training Dataset ◽

Learning Approaches ◽

Learning Models ◽

Sensitivity Maps

Deep learning approaches have attracted a lot of attention in the automatic detection of Covid-19 and transfer learning is the most common approach. However, majority of the pre-trained models are trained on color images, which can cause inefficiencies when fine-tuning the models on Covid-19 images which are often grayscale. To address this issue, we propose a deep learning architecture called CovidNet which requires a relatively smaller number of parameters. CovidNet accepts grayscale images as inputs and is suitable for training with limited training dataset. Experimental results show that CovidNet outperforms other state-of-the-art deep learning models for Covid-19 detection.

Download Full-text

Pansformers: Transformer-Based Self-Attention Network for Pansharpening

10.36227/techrxiv.17153228.v1 ◽

2021 ◽

Author(s):

Nithin G R ◽

Nitish Kumar M ◽

Venkateswaran Narasimhan ◽

Rajanikanth Kakani ◽

Ujjwal Gupta ◽

...

Keyword(s):

Deep Learning ◽

High Resolution ◽

Performance Metrics ◽

Satellite Image ◽

Image Data ◽

Landsat 8 ◽

Learning Models ◽

Attention Network ◽

Panchromatic Image ◽

Recent Success

Pansharpening is the task of creating a High-Resolution Multi-Spectral Image (HRMS) by extracting and infusing pixel details from the High-Resolution Panchromatic Image into the Low-Resolution Multi-Spectral (LRMS). With the boom in the amount of satellite image data, researchers have replaced traditional approaches with deep learning models. However, existing deep learning models are not built to capture intricate pixel-level relationships. Motivated by the recent success of self-attention mechanisms in computer vision tasks, we propose Pansformers, a transformer-based self-attention architecture, that computes band-wise attention. A further improvement is proposed in the attention network by introducing a Multi-Patch Attention mechanism, which operates on non-overlapping, local patches of the image. Our model is successful in infusing relevant local details from the Panchromatic image while preserving the spectral integrity of the MS image. We show that our Pansformer model significantly improves the performance metrics and the output image quality on imagery from two satellite distributions IKONOS and LANDSAT-8.

Download Full-text