Charting the State-of-the-Art in the Application of Convolutional Neural Networks to Quality Control in Industry 4.0 and Smart Manufacturing

In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.

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LdsConv: Learned Depthwise Separable Convolutions by Group Pruning

Sensors ◽

10.3390/s20154349 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4349

Author(s):

Wenxiang Lin ◽

Yan Ding ◽

Hua-Liang Wei ◽

Xinglin Pan ◽

Yutong Zhang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

State Of The Art ◽

Computational Cost ◽

The State ◽

Direct Replacement ◽

Improved Accuracy ◽

Pruning Technique ◽

Strong Capacity

Standard convolutional filters usually capture unnecessary overlap of features resulting in a waste of computational cost. In this paper, we aim to solve this problem by proposing a novel Learned Depthwise Separable Convolution (LdsConv) operation that is smart but has a strong capacity for learning. It integrates the pruning technique into the design of convolutional filters, formulated as a generic convolutional unit that can be used as a direct replacement of convolutions without any adjustments of the architecture. To show the effectiveness of the proposed method, experiments are carried out using the state-of-the-art convolutional neural networks (CNNs), including ResNet, DenseNet, SE-ResNet and MobileNet, respectively. The results show that by simply replacing the original convolution with LdsConv in these CNNs, it can achieve a significantly improved accuracy while reducing computational cost. For the case of ResNet50, the FLOPs can be reduced by 40.9%, meanwhile the accuracy on the associated ImageNet increases.

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Optimizing 3D Convolution Kernels on Stereo Matching for Resource Efficient Computations

Sensors ◽

10.3390/s21206808 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6808

Author(s):

Jianqiang Xiao ◽

Dianbo Ma ◽

Satoshi Yamane

Keyword(s):

Neural Networks ◽

Computational Complexity ◽

Convolutional Neural Networks ◽

Stereo Matching ◽

State Of The Art ◽

Computational Cost ◽

The State ◽

Matching Network ◽

Convolution Kernels ◽

Low Computational Cost

Despite recent stereo matching algorithms achieving significant results on public benchmarks, the problem of requiring heavy computation remains unsolved. Most works focus on designing an architecture to reduce the computational complexity, while we take aim at optimizing 3D convolution kernels on the Pyramid Stereo Matching Network (PSMNet) for solving the problem. In this paper, we design a series of comparative experiments exploring the performance of well-known convolution kernels on PSMNet. Our model saves the computational complexity from 256.66G MAdd (Multiply-Add operations) to 69.03G MAdd (198.47G MAdd to 10.84G MAdd for only considering 3D convolutional neural networks) without losing accuracy. On Scene Flow and KITTI 2015 datasets, our model achieves results comparable to the state-of-the-art with a low computational cost.

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Common Kernels and Convolutions in Binary- and Ternary-Weight Neural Networks

Journal of Circuits System and Computers ◽

10.1142/s0218126621501589 ◽

2020 ◽

pp. 2150158

Author(s):

Byungmin Ahn ◽

Taewhan Kim

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Optimization Algorithm ◽

State Of The Art ◽

The State ◽

Memory Access ◽

Experimental Results ◽

Hardware Platform ◽

Access Latency ◽

The Common

A new algorithm for extracting common kernels and convolutions to maximally eliminate the redundant operations among the convolutions in binary- and ternary-weight convolutional neural networks is presented. Precisely, we propose (1) a new algorithm of common kernel extraction to overcome the local and limited exploration of common kernel candidates by the existing method, and subsequently apply (2) a new concept of common convolution extraction to maximally eliminate the redundancy in the convolution operations. In addition, our algorithm is able to (3) tune in minimizing the number of resulting kernels for convolutions, thereby saving the total memory access latency for kernels. Experimental results on ternary-weight VGG-16 demonstrate that our convolution optimization algorithm is very effective, reducing the total number of operations for all convolutions by [Formula: see text], thereby reducing the total number of execution cycles on hardware platform by 22.4% while using [Formula: see text] fewer kernels over that of the convolution utilizing the common kernels extracted by the state-of-the-art algorithm.

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Recurrent convolutional neural networks for poet identification

Digital Scholarship in the Humanities ◽

10.1093/llc/fqz096 ◽

2020 ◽

Cited By ~ 1

Author(s):

Dariush Salami ◽

Saeedeh Momtazi

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Language Processing ◽

Short Term Memory ◽

State Of The Art ◽

The State ◽

Support Vector ◽

Neural Network Models ◽

Spatial Features ◽

Input Text

Abstract Deep neural networks have been widely used in various language processing tasks. Recurrent neural networks (RNNs) and convolutional neural networks (CNN) are two common types of neural networks that have a successful history in capturing temporal and spatial features of texts. By using RNN, we can encode input text to a lower space of semantic features while considering the sequential behavior of words. By using CNN, we can transfer the representation of input text to a flat structure to be used for classifying text. In this article, we proposed a novel recurrent CNN model to capture not only the temporal but also the spatial features of the input poem/verse to be used for poet identification. Considering the shortcomings of the normal RNNs, we try both long short-term memory and gated recurrent unit units in the proposed architecture and apply them to the poet identification task. There are a large number of poems in the history of literature whose poets are unknown. Considering the importance of the task in the information processing field, a great variety of methods from traditional learning models, such as support vector machine and logistic regression, to deep neural network models, such as CNN, have been proposed to address this problem. Our experiments show that the proposed model significantly outperforms the state-of-the-art models for poet identification by receiving either a poem or a single verse as input. In comparison to the state-of-the-art CNN model, we achieved 9% and 4% improvements in f-measure for poem- and verse-based tasks, respectively.

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Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/309 ◽

2018 ◽

Cited By ~ 79

Author(s):

Yang He ◽

Guoliang Kang ◽

Xuanyi Dong ◽

Yanwei Fu ◽

Yi Yang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

State Of The Art ◽

The State ◽

Training Data ◽

Deep Convolutional Neural Networks ◽

Inference Procedure ◽

Accuracy Improvement ◽

Large Capacity ◽

Pruning Methods

This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables the pruned filters to be updated when training the model after pruning. SFP has two advantages over previous works: (1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger model capacity to learn from the training data. (2) Less dependence on the pretrained model. Large capacity enables SFP to train from scratch and prune the model simultaneously. In contrast, previous filter pruning methods should be conducted on the basis of the pre-trained model to guarantee their performance. Empirically, SFP from scratch outperforms the previous filter pruning methods. Moreover, our approach has been demonstrated effective for many advanced CNN architectures. Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art. Code is publicly available on GitHub: https://github.com/he-y/softfilter-pruning

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Real-Time Person Segmentation – Based on Body Pix

International Journal for Modern Trends in Science and Technology - RTT2020 ◽

10.46501/ijmtst061201 ◽

2020 ◽

Vol 6 (12) ◽

pp. 1-7

Author(s):

Anish Mankotia and Meenu Garg

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

State Of The Art ◽

Semantic Segmentation ◽

The State ◽

The Body ◽

Surface Information ◽

Semantic Class ◽

Additional Information

In this paper, we propose a novel semantic segmentation-based on the body pix module of the Tensor flow.js which can keep up with the accuracy of the state-of-the art approaches while running in real time. The solution follows the convolutional neural networks, each step in the workflow being enhanced by additional information from semantic segmentation. Therefore, we introduce several improvements to computation, aggregation, and optimization by adapting existing techniques to integrate additional surface information given by each semantic class. Using the body pix model which is trained using CNN, the ResNET50, this network can work with more than 150 layers, removing the problem of vanishing gradients. Using this network our body pix module, creates a more accurate and defined segmentation, and also supports multi-person segmentation.

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AutoLinker: Automatic Fragment Linking with Deep Conditional Transformer Neural Networks

10.26434/chemrxiv.12271508.v2 ◽

2020 ◽

Author(s):

Yuyao Yang ◽

Shuangjia Zheng ◽

Shimin Su ◽

Jun Xu ◽

Hongming Chen

Keyword(s):

Neural Networks ◽

Drug Discovery ◽

Drug Design ◽

State Of The Art ◽

The State ◽

Generative Model ◽

Lead Optimization ◽

Scaffold Hopping ◽

Reference Models ◽

Lead Generation

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.

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Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02376-3 ◽

2021 ◽

Author(s):

Jorge F. Lazo ◽

Aldo Marzullo ◽

Sara Moccia ◽

Michele Catellani ◽

Benoit Rosa ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

State Of The Art ◽

Automatic Segmentation ◽

Temporal Information ◽

Invasive Technique ◽

Dice Similarity Coefficient ◽

Specular Reflections ◽

Lumen Segmentation ◽

Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

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Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽

10.3390/math9060624 ◽

2021 ◽

Vol 9 (6) ◽

pp. 624

Author(s):

Stefan Rohrmanstorfer ◽

Mikhail Komarov ◽

Felix Mödritscher

Keyword(s):

Neural Networks ◽

Feature Extraction ◽

Image Classification ◽

Convolutional Neural Networks ◽

Data Augmentation ◽

State Of The Art ◽

Image Data ◽

Classification Model ◽

Upper Body ◽

Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

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