scholarly journals Human-Like Sketch Object Recognition via Analogical Learning

2019 ◽  
Vol 33 ◽  
pp. 1336-1343
Author(s):  
Kezhen Chen ◽  
Irina Rabkina ◽  
Matthew D. McLure ◽  
Kenneth D. Forbus
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Recognition ◽  
Image Recognition ◽  
Visual Representations ◽  
Learning Systems ◽  
Training Data ◽  
Adversarial Examples ◽  
Analogical Learning

Deep learning systems can perform well on some image recognition tasks. However, they have serious limitations, including requiring far more training data than humans do and being fooled by adversarial examples. By contrast, analogical learning over relational representations tends to be far more data-efficient, requiring only human-like amounts of training data. This paper introduces an approach that combines automatically constructed qualitative visual representations with analogical learning to tackle a hard computer vision problem, object recognition from sketches. Results from the MNIST dataset and a novel dataset, the Coloring Book Objects dataset, are provided. Comparison to existing approaches indicates that analogical generalization can be used to identify sketched objects from these datasets with several orders of magnitude fewer examples than deep learning systems require.

scholarly journals BUILDING GENERALIZATION USING DEEP LEARNING

2018 ◽  
Vol XLII-4 ◽  
pp. 565-572 ◽  
Author(s):  
M. Sester ◽  
Y. Feng ◽  
F. Thiemann
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Physical Reality ◽  
Training Data ◽  
Data Sets ◽  
Learning Approaches ◽  
Depth Analysis ◽  
Map Series ◽  
Training Examples ◽  
Future Work

<p><strong>Abstract.</strong> Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g. simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the benchmark is the human operator, who is able to design an aesthetic and correct representation of the physical reality.</p><p>Deep Learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform the traditional computer vision methods. In both domains &amp;ndash; computer vision and cartography &amp;ndash; humans are able to produce a solution; a prerequisite for this is, that there is the possibility to generate many training examples for the different cases. Thus, the idea in this paper is to employ Deep Learning for cartographic generalizations tasks, especially for the task of building generalization. An advantage of this task is the fact that many training data sets are available from given map series. The approach is a first attempt using an existing network.</p><p>In the paper, the details of the implementation will be reported, together with an in depth analysis of the results. An outlook on future work will be given.</p>

scholarly journals Really natural adversarial examples

2021 ◽  
Author(s):  
Anibal Pedraza ◽  
Oscar Deniz ◽  
Gloria Bueno
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Recognition ◽  
Image Quality ◽  
Real World ◽  
Deep Neural Networks ◽  
Distance Metrics ◽  
The Real ◽  
Adversarial Examples ◽  
General Object

AbstractThe phenomenon of Adversarial Examples has become one of the most intriguing topics associated to deep learning. The so-called adversarial attacks have the ability to fool deep neural networks with inappreciable perturbations. While the effect is striking, it has been suggested that such carefully selected injected noise does not necessarily appear in real-world scenarios. In contrast to this, some authors have looked for ways to generate adversarial noise in physical scenarios (traffic signs, shirts, etc.), thus showing that attackers can indeed fool the networks. In this paper we go beyond that and show that adversarial examples also appear in the real-world without any attacker or maliciously selected noise involved. We show this by using images from tasks related to microscopy and also general object recognition with the well-known ImageNet dataset. A comparison between these natural and the artificially generated adversarial examples is performed using distance metrics and image quality metrics. We also show that the natural adversarial examples are in fact at a higher distance from the originals that in the case of artificially generated adversarial examples.

scholarly journals Data Augmentation Method by Applying Color Perturbation of Inverse PSNR and Geometric Transformations for Object Recognition Based on Deep Learning

2020 ◽  
Vol 10 (11) ◽  
pp. 3755
Author(s):  
Eun Kyeong Kim ◽  
Hansoo Lee ◽  
Jin Yong Kim ◽  
Sungshin Kim
Keyword(s):  
Deep Learning ◽  
Object Recognition ◽  
Data Augmentation ◽  
Color Space ◽  
Original Data ◽  
Training Data ◽  
Geometric Transformation ◽  
Geometric Transformations ◽  
Background Elimination ◽  
Deep Learning Network

Deep learning is applied in various manufacturing domains. To train a deep learning network, we must collect a sufficient amount of training data. However, it is difficult to collect image datasets required to train the networks to perform object recognition, especially because target items that are to be classified are generally excluded from existing databases, and the manual collection of images poses certain limitations. Therefore, to overcome the data deficiency that is present in many domains including manufacturing, we propose a method of generating new training images via image pre-processing steps, background elimination, target extraction while maintaining the ratio of the object size in the original image, color perturbation considering the predefined similarity between the original and generated images, geometric transformations, and transfer learning. Specifically, to demonstrate color perturbation and geometric transformations, we compare and analyze the experiments of each color space and each geometric transformation. The experimental results show that the proposed method can effectively augment the original data, correctly classify similar items, and improve the image classification accuracy. In addition, it also demonstrates that the effective data augmentation method is crucial when the amount of training data is small.

scholarly journals How to Generate Image Dataset based on 3D Model and Deep Learning Method

2018 ◽  
Vol 7 (3.34) ◽  
pp. 221
Author(s):  
Sooyoung Cho ◽  
Sang Geun Choi ◽  
Daeyeol Kim ◽  
Gyunghak Lee ◽  
Chae BongSohn
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Recognition ◽  
Object Tracking ◽  
3D Model ◽  
Object Segmentation ◽  
Learning Method ◽  
Data Set ◽  
Image Dataset ◽  
Deep Learning Model

Performances of computer vision tasks have been drastically improved after applying deep learning. Such object recognition, object segmentation, object tracking, and others have been approached to the super-human level. Most of the algorithms were trained by using supervised learning. In general, the performance of computer vision is improved by increasing the size of the data. The collected data was labeled and used as a data set of the YOLO algorithm. In this paper, we propose a data set generation method using Unity which is one of the 3D engines. The proposed method makes it easy to obtain the data necessary for learning. We classify 2D polymorphic objects and test them against various data using a deep learning model. In the classification using CNN and VGG-16, 90% accuracy was achieved. And we used Tiny-YOLO of YOLO algorithm for object recognition and we achieved 78% accuracy. Finally, we compared in terms of virtual and real environments it showed a result of 97 to 99 percent for each accuracy.

scholarly journals DECIMER - Towards Deep Learning for Chemical Image Recognition

2020 ◽  
Author(s):  
Kohulan Rajan ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Open Data ◽  
Data Representation ◽  
Training Data ◽  
Training Time ◽  
Training Structures ◽  
Training Success ◽  
Traditional Approaches ◽  
Chemical Image

The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of DECIMER (Deep lEarning for Chemical ImagE Recognition), a deep learning method based on existing show-and-tell deep neural networks which makes very few assumptions about the structure of the underlying problem. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are clearly superior over SMILES and we have preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggest that we might be able to achieve >90% accuracy with about 60 to 100 million training structures, so that training can be completed within several months on a single GPU. This work is completely based on open-source software and open data and is available to the general public for any purpose.

scholarly journals Adversarial Examples – Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Abdur Rahman ◽  
M. Shamim Hossain ◽  
Nabil A. Alrajeh ◽  
Fawaz Alsolami
Keyword(s):  
Deep Learning ◽  
Learning Systems ◽  
Security Threats ◽  
Adversarial Examples ◽  
Iot Devices

scholarly journals DECIMER: towards deep learning for chemical image recognition

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Kohulan Rajan ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Open Data ◽  
Data Representation ◽  
Training Data ◽  
Training Time ◽  
Training Structures ◽  
Training Success ◽  
Traditional Approaches ◽  
Chemical Image

Abstract The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of Deep lEarning for Chemical ImagE Recognition (DECIMER), a deep learning method based on existing show-and-tell deep neural networks, which makes very few assumptions about the structure of the underlying problem. It translates a bitmap image of a molecule, as found in publications, into a SMILES. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are superior over SMILES and we have a preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggests that we might be able to achieve near-accurate prediction with 50 to 100 million training structures. This work is entirely based on open-source software and open data and is available to the general public for any purpose.

scholarly journals DECIMER - Towards Deep Learning for Chemical Image Recognition

2020 ◽  
Author(s):  
Kohulan Rajan ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Open Data ◽  
Data Representation ◽  
Training Data ◽  
Training Time ◽  
Training Structures ◽  
Training Success ◽  
Traditional Approaches ◽  
Chemical Image

The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of DECIMER (Deep lEarning for Chemical ImagE Recognition), a deep learning method based on existing show-and-tell deep neural networks which makes very few assumptions about the structure of the underlying problem. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are clearly superior over SMILES and we have preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggest that we might be able to achieve >90% accuracy with about 60 to 100 million training structures, so that training can be completed within several months on a single GPU. This work is completely based on open-source software and open data and is available to the general public for any purpose.

scholarly journals A FULLY AUTOMATED PIPELINE FOR CLASSIFICATION TASKS WITH AN APPLICATION TO REMOTE SENSING

2016 ◽  
Vol XLI-B3 ◽  
pp. 923-929
Author(s):  
K. Suzuki ◽  
M. Claesen ◽  
H. Takeda ◽  
B. De Moor
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Object Recognition ◽  
Character Recognition ◽  
Domain Knowledge ◽  
High Performance ◽  
A Priori ◽  
Training Data ◽  
Classification Problems

Nowadays deep learning has been intensively in spotlight owing to its great victories at major competitions, which undeservedly pushed ‘shallow’ machine learning methods, relatively naive/handy algorithms commonly used by industrial engineers, to the background in spite of their facilities such as small requisite amount of time/dataset for training. We, with a practical point of view, utilized shallow learning algorithms to construct a learning pipeline such that operators can utilize machine learning without any special knowledge, expensive computation environment, and a large amount of labelled data. The proposed pipeline automates a whole classification process, namely feature-selection, weighting features and the selection of the most suitable classifier with optimized hyperparameters. The configuration facilitates particle swarm optimization, one of well-known metaheuristic algorithms for the sake of generally fast and fine optimization, which enables us not only to optimize (hyper)parameters but also to determine appropriate features/classifier to the problem, which has conventionally been a priori based on domain knowledge and remained untouched or dealt with naïve algorithms such as grid search. Through experiments with the MNIST and CIFAR-10 datasets, common datasets in computer vision field for character recognition and object recognition problems respectively, our automated learning approach provides high performance considering its simple setting (i.e. non-specialized setting depending on dataset), small amount of training data, and practical learning time. Moreover, compared to deep learning the performance stays robust without almost any modification even with a remote sensing object recognition problem, which in turn indicates that there is a high possibility that our approach contributes to general classification problems.

scholarly journals DECIMER - Towards Deep Learning for Chemical Image Recognition

2020 ◽  
Author(s):  
Kohulan Rajan ◽  
Achim Zielesny ◽  
Christoph Steinbeck
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Open Data ◽  
Data Representation ◽  
Training Data ◽  
Training Time ◽  
Training Structures ◽  
Training Success ◽  
Traditional Approaches ◽  
Chemical Image

The automatic recognition of chemical structure diagrams from the literature is an indispensable component of workflows to re-discover information about chemicals and to make it available in open-access databases. Here we report preliminary findings in our development of DECIMER (Deep lEarning for Chemical ImagE Recognition), a deep learning method based on existing show-and-tell deep neural networks which makes very few assumptions about the structure of the underlying problem. The training state reported here does not yet rival the performance of existing traditional approaches, but we present evidence that our method will reach a comparable detection power with sufficient training time. Training success of DECIMER depends on the input data representation: DeepSMILES are clearly superior over SMILES and we have preliminary indication that the recently reported SELFIES outperform DeepSMILES. An extrapolation of our results towards larger training data sizes suggest that we might be able to achieve >90% accuracy with about 60 to 100 million training structures, so that training can be completed within several months on a single GPU. This work is completely based on open-source software and open data and is available to the general public for any purpose.

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