Relevant bibliographies by topics / Dilated convolution

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Dilated convolution'

Author: Grafiati
Published: 24 August 2024

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Journal articles on the topic "Dilated convolution"

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Wang, Wei, Yiyang Hu, Ting Zou, Hongmei Liu, Jin Wang, and Xin Wang. "A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/8817849.

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Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilated convolutions are introduced into a specific convolutional layer of the MobileNet model. Without increasing the number of parameters, dilated convolutions are used to increase the receptive field of the convolution filters to obtain better classification accuracy. The experiments were performed on the Caltech-101, Caltech-256, and Tubingen animals with attribute datasets, respectively. The results show that Dilated-MobileNets can obtain up to 2% higher classification accuracy than MobileNet.
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Peng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong, and Benli Yu. "Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones." Journal of Physics: Conference Series 2246, no. 1 (April 1, 2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.

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Abstract In this paper, several improved convolutional recurrent networks (CRN) are proposed, which can enhance the speech with non-additive distortion captured by fiber-optic microphones. Our preliminary study shows that the original CRN structure based on amplitude spectrum estimation is seriously distorted due to the loss of phase information. Therefore, we transform the network to run in time domain and gain 0.42 improvement on PESQ and 0.03 improvement on STOI. In addition, we integrate dilated convolution into CRN architecture, and adopt three different types of bottleneck modules, namely long short-term memory (LSTM), gated recurrent units (GRU) and dilated convolutions. The experimental results show that the model with dilated convolution in the encoder-decoder and the model with dilated convolution at bottleneck layer have the highest PESQ and STOI scores, respectively.
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Zhao, Feng, Junjie Zhang, Zhe Meng, and Hanqiang Liu. "Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification." Remote Sensing 13, no. 17 (August 26, 2021): 3396. http://dx.doi.org/10.3390/rs13173396.

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Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral–spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models.
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Chim, Seyha, Jin-Gu Lee, and Ho-Hyun Park. "Dilated Skip Convolution for Facial Landmark Detection." Sensors 19, no. 24 (December 4, 2019): 5350. http://dx.doi.org/10.3390/s19245350.

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Facial landmark detection has gained enormous interest for face-related applications due to its success in facial analysis tasks such as facial recognition, cartoon generation, face tracking and facial expression analysis. Many studies have been proposed and implemented to deal with the challenging problems of localizing facial landmarks from given images, including large appearance variations and partial occlusion. Studies have differed in the way they use the facial appearances and shape information of input images. In our work, we consider facial information within both global and local contexts. We aim to obtain local pixel-level accuracy for local-context information in the first stage and integrate this with knowledge of spatial relationships between each key point in a whole image for global-context information in the second stage. Thus, the pipeline of our architecture consists of two main components: (1) a deep network for local-context subnet that generates detection heatmaps via fully convolutional DenseNets with additional kernel convolution filters and (2) a dilated skip convolution subnet—a combination of dilated convolutions and skip-connections networks—that are in charge of robustly refining the local appearance heatmaps. Through this proposed architecture, we demonstrate that our approach achieves state-of-the-art performance on challenging datasets—including LFPW, HELEN, 300W and AFLW2000-3D—by leveraging fully convolutional DenseNets, skip-connections and dilated convolution architecture without further post-processing.
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Song, Zhendong, Yupeng Ma, Fang Tan, and Xiaoyi Feng. "Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement." Applied Sciences 12, no. 7 (March 29, 2022): 3461. http://dx.doi.org/10.3390/app12073461.

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In this paper, we propose a fully convolutional neural network based on recursive recurrent convolution for monaural speech enhancement in the time domain. The proposed network is an encoder-decoder structure using a series of hybrid dilated modules (HDM). The encoder creates low-dimensional features of a noisy input frame. In the HDM, the dilated convolution is used to expand the receptive field of the network model. In contrast, the standard convolution is used to make up for the under-utilized local information of the dilated convolution. The decoder is used to reconstruct enhanced frames. The recursive recurrent convolutional network uses GRU to solve the problem of multiple training parameters and complex structures. State-of-the-art results are achieved on two commonly used speech datasets.
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Tang, Jingfan, Meijia Zhou, Pengfei Li, Min Zhang, and Ming Jiang. "Crowd Counting Based on Multiresolution Density Map and Parallel Dilated Convolution." Scientific Programming 2021 (January 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/8831458.

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The current crowd counting tasks rely on a fully convolutional network to generate a density map that can achieve good performance. However, due to the crowd occlusion and perspective distortion in the image, the directly generated density map usually neglects the scale information and spatial contact information. To solve it, we proposed MDPDNet (Multiresolution Density maps and Parallel Dilated convolutions’ Network) to reduce the influence of occlusion and distortion on crowd estimation. This network is composed of two modules: (1) the parallel dilated convolution module (PDM) that combines three dilated convolutions in parallel to obtain the deep features on the larger receptive field with fewer parameters while reducing the loss of multiscale information; (2) the multiresolution density map module (MDM) that contains three-branch networks for extracting spatial contact information on three different low-resolution density maps as the feature input of the final crowd density map. Experiments show that MDPDNet achieved excellent results on three mainstream datasets (ShanghaiTech, UCF_CC_50, and UCF-QNRF).
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Zhang, Jianming, Chaoquan Lu, Jin Wang, Lei Wang, and Xiao-Guang Yue. "Concrete Cracks Detection Based on FCN with Dilated Convolution." Applied Sciences 9, no. 13 (July 1, 2019): 2686. http://dx.doi.org/10.3390/app9132686.

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In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the input image, designed the dilated convolutions with different dilation rates to extract the feature maps of different receptive fields, and fused the extracted features from multiple branches. Then, we exploited the stacked deconvolution to do up-sampling operator in the fused feature maps. Finally, we used the SoftMax function to classify the feature maps at the pixel level. In order to verify the validity of the model, we introduced the commonly used evaluation indicators of semantic segmentation: Pixel Accuracy (PA), Mean Pixel Accuracy (MPA), Mean Intersection over Union (MIoU), and Frequency Weighted Intersection over Union (FWIoU). The experimental results show that the proposed model converges faster and has better generalization performance on the test set by introducing dilated convolutions with different dilation rates and a multi-branch fusion strategy. Our model has a PA of 96.84%, MPA of 92.55%, MIoU of 86.05% and FWIoU of 94.22% on the test set, which is superior to other models.
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Cao, Ruifen, Xi Pei, Ning Ge, and Chunhou Zheng. "Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382110342. http://dx.doi.org/10.1177/15330338211034284.

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Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid training and testing. A dilated convolution module for extracting multiscale context features containing the original information on fine texture and boundaries is integrated into the U-network architecture to avoid information loss due to down-sampling and improve the segmentation accuracy. In addition, batch normalization is added to the deep dilated convolutional U-network for fast and stable convergence. In the present study, the training and validation loss tended to be stable after 40 training epochs. This deep dilated convolutional U-network model was able to segment the clinical target volume with an overall mean Dice similarity coefficient of 86.7% and a respective 95% Hausdorff distance of 37.4 mm, indicating reasonable volume overlap of the auto-segmented and manual contours. The mean Cohen kappa coefficient was 0.863, indicating that the deep dilated convolutional U-network was robust. Comparisons with the U-network and attention U-network showed that the overall performance of the deep dilated convolutional U-network was best for the Dice similarity coefficient, 95% Hausdorff distance, and Cohen kappa coefficient. The test time for segmentation of the clinical target volume was approximately 25 seconds per patient. This deep dilated convolutional U-network could be applied in the clinical setting to save time in delineation and improve the consistency of contouring.
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Wang, Ran, Ruyu Shi, Xiong Hu, and Changqing Shen. "Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks." Shock and Vibration 2021 (January 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/6616861.

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Remaining useful life (RUL) prediction is necessary for guaranteeing machinery’s safe operation. Among deep learning architectures, convolutional neural network (CNN) has shown achievements in RUL prediction because of its strong ability in representation learning. Features from different receptive fields extracted by different sizes of convolution kernels can provide complete information for prognosis. The single size convolution kernel in traditional CNN is difficult to learn comprehensive information from complex signals. Besides, the ability to learn local and global features synchronously is limited to conventional CNN. Thus, a multiscale convolutional neural network (MS-CNN) is introduced to overcome these aforementioned problems. Convolution filters with different dilation rates are integrated to form a dilated convolution block, which can learn features in different receptive fields. Then, several stacked integrated dilated convolution blocks in different depths are concatenated to extract local and global features. The effectiveness of the proposed method is verified by a bearing dataset prepared from the PRONOSTIA platform. The results turn out that the proposed MS-CNN has higher prediction accuracy than many other deep learning-based RUL methods.
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Madych, W. R. "Limits of Dilated Convolution Transforms." SIAM Journal on Mathematical Analysis 16, no. 3 (May 1985): 551–58. http://dx.doi.org/10.1137/0516041.

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Dissertations / Theses on the topic "Dilated convolution"

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Khalfaoui, Hassani Ismail. "Convolution dilatée avec espacements apprenables." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES017.

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Dans cette thèse, nous avons développé et étudié la méthode de convolution dilatée avec espacements apprenables (Dilated Convolution with Learnable Spacings en anglais, qu'on abrégera par le sigle DCLS). La méthode DCLS peut être considérée comme une extension de la méthode de convolution dilatée standard, mais dans laquelle les positions des poids d'un réseau de neurones sont apprises grâce à l'algorithme de rétropropagation du gradient, et ce, à l'aide d'une technique d'interpolation. Par suite, nous avons démontré empiriquement l'efficacité de la méthode DCLS en fournissant des preuves concrètes, issues de nombreuses expériences en apprentissage supervisé. Ces expériences sont issues des domaines de la vision par ordinateur, de l'audio et du traitement de la parole et toutes montrent que la méthode DCLS a un avantage compétitif sur les techniques standards de convolution ainsi que sur plusieurs méthodes de convolution avancées. Notre approche s'est faite en plusieurs étapes, en commençant par une analyse de la littérature et des techniques de convolution existantes qui ont précédé le développement de la méthode DCLS. Nous nous sommes particulièrement intéressés aux méthodes étroitement liées à la nôtre et qui demeurent essentielles pour saisir les nuances ainsi que le caractère unique de notre approche. La pierre angulaire de notre étude repose sur l'introduction et l'application de la méthode DCLS aux réseaux neuronaux convolutifs (CNN), mais aussi aux architectures hybrides qui se basent à la fois sur des méthodes convolutives et des méthodes d'attention visuelle. La méthode DCLS est particulièrement remarquable pour ses capacités dans les tâches supervisées de vision par ordinateur telles que la classification, la segmentation et la détection d'objets, qui sont toutes des tâches essentielles dans ce domaine. Ayant développé la méthode DCLS à l'origine avec une interpolation bilinéaire, nous avons entrepris l'exploration d'autres méthodes d'interpolation susceptibles de remplacer l'interpolation bilinéaire, traditionnellement utilisée dans DCLS, ainsi que d'autres méthodes de convolution, et qui visent à rendre différentiables les paramètres de positions des poids dans le noyau de convolution. L'interpolation gaussienne s'est avérée être légèrement meilleure en termes de performances. Notre recherche nous a amené par la suite à appliquer la méthode DCLS dans le domaine des réseaux de neurones à spikes (SNN) afin de permettre l'apprentissage des délais synaptiques à l'intérieur d'un réseau de neurones qui pourrait être éventuellement transféré à des puces dites neuromorphiques. Les résultats montrent que la méthode DCLS se tient comme nouvel état de l'art des SNNs en classification audio pour certaines tâches de référence dans ce domaine. Ces dernières tâches portent sur des ensembles de données connus pour avoir une composante temporelle importante. En outre, nous montrons aussi que DCLS permet d'améliorer de manière significative la précision des réseaux neuronaux artificiels pour la tâche de classification audio multi-label, un aboutissement clé dans l'un des benchmarks de classification audio les plus importants. Enfin, nous concluons par une discussion sur le dispositif expérimental choisi, ses limites, les limites de notre méthode et nos résultats
In this thesis, we develop and study the Dilated Convolution with Learnable Spacings (DCLS) method. The DCLS method can be considered as an extension of the standard dilated convolution method, but in which the positions of the weights of a neural network are learned during training by the gradient backpropagation algorithm, thanks to an interpolation technique. We empirically demonstrate the effectiveness of the DCLS method by providing concrete evidence from numerous supervised learning experiments. These experiments are drawn from the fields of computer vision, audio, and speech processing, and all show that the DCLS method has a competitive advantage over standard convolution techniques, as well as over several advanced convolution methods. Our approach is structured in several steps, starting with an analysis of the literature and existing convolution techniques that preceded the development of the DCLS method. We were particularly interested in the methods that are closely related to our own and that remain essential to capture the nuances and uniqueness of our approach. The cornerstone of our study is the introduction and application of the DCLS method to convolutional neural networks (CNNs), as well as to hybrid architectures that rely on both convolutional and visual attention approaches. The DCLS method is particularly noteworthy for its capabilities in supervised computer vision tasks such as classification, semantic segmentation, and object detection, all of which are essential tasks in the field. Having originally developed the DCLS method with bilinear interpolation, we explored other interpolation methods that could replace the bilinear interpolation conventionally used in DCLS, and which aim to make the position parameters of the weights in the convolution kernel differentiable. Gaussian interpolation proved to be slightly better in terms of performance. Our research then led us to apply the DCLS method in the field of spiking neural networks (SNNs) to enable synaptic delay learning within a neural network that could eventually be transferred to so-called neuromorphic chips. The results show that the DCLS method stands out as a new state-of-the-art technique in SNN audio classification for certain benchmark tasks in this field. These tasks involve datasets with a high temporal component. In addition, we show that DCLS can significantly improve the accuracy of artificial neural networks for the multi-label audio classification task, a key achievement in one of the most important audio classification benchmarks. We conclude with a discussion of the chosen experimental setup, its limitations, the limitations of our method, and our results
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Highlander, Tyler Clayton. "Conditional Dilated Attention Tracking Model - C-DATM." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1564652134758139.

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Börjesson, Lukas. "Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167331.

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In this paper, predictions of future price movements of a major American stock index was made by analysing past movements of the same and other correlated indices. A model that has shown very good results in speech recognition was modified to suit the analysis of financial data and was then compared to a base model, restricted by assumptions made for an efficient market. The performance of any model, that is trained by looking at past observations, is heavily influenced by how the division of the data into train, validation and test sets is made. This is further exaggerated by the temporal structure of the financial data, which means that the causal relationship between the predictors and the response is dependent in time. The complexity of the financial system further increases the struggle to make accurate predictions, but the model suggested here was still able to outperform the naive base model by more than 20 percent. The model is, however, too primitive to be used as a trading system, but suitable modifications, in order to turn the model into one, will be discussed in the end of the paper.
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Yeh, Pin-Yi, and 葉品儀. "Multi-Scale Neural Network with Dilated Convolutions for Image Deblurring." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/vgs5cw.

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碩士
國立臺灣科技大學
資訊工程系
107
Several deep learning-based approaches are successful in single image deblurring, particularly, convolutional neural networks (CNN). Unlike traditional methods which try to estimate the blur kernel to extract the latent sharp image, CNN-based methods can directly find the mapping from the blurry input image to the latent sharp image. CNN usually has many layers to represent complex spatial relationships, and down-sampling layers are used to reduce the number of parameters (e.g., encoder-decoder architecture). However, down-sampling causes some spatial information to be lost, and this information could be useful in deblurring large regions. The receptive field is the spatial coverage of each feature, and increasing its value allows less loss of spatial information. We used dilated convolution to increase the receptive field of the features without increasing the number of parameters. Furthermore, the "coarse-to-fine" strategy is applied to the network to the blurry input image at different scales in this thesis. By using this strategy, we can progressively improve the outputs, and allow us to capture details from different scales, without adding more parameters. We show that the proposed model not only has better results with the state-of-the-art but also has faster execution time.
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Liu, Chien-Chung, and 劉建忠. "Improved Image Super Resolution Technology Based on Dilated Convolutional Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w6cn2k.

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碩士
國立臺中科技大學
資訊工程系碩士班
106
Image super resolution is wide application in image processing and computer vision. Because original super resolution image can’t be irreversible and it have distorted pixel values after the image is enlarged are challenging subjects. This paper proposed two architectures which is using convolutional neural network architecture of deep learning to carry out image super resolution. They estimate pixels of super resolution image by neurons of convolutional neural network. The first architecture is reduced dilated convolutional neural network. It reduces dilated convolutional neural network to six convolutional layers. In the second layer to fourth layer use convolution of double dilated rate. The first layer output concatenates the fourth layer output and the second layer output concatenates the third layer output are to deeper learning. The other is wide dilated convolutional neural network. It lets input pass convolutions of difference dilated rate to get output. It achieves wide learning. Neural network learns convolutional input of difference dilated rate by concatenating two outputs to be input of next layer at the same time. It is able to more detail feature extraction and achieve effect of wide learning. This experiments use the parameters of convolutional neural network employed dilated convolutional neural network architecture. The experimental parameters include epoch, validation split, validation mode, sub image size, sub image number, batch size. The experiments appoint appropriate parameters to be 500 epoch, 0.2 validation split, random single sub image which is sub images of the image, 41×41 sub image size, 50 sub image number, 64 batch size. Experimental results appoint PSNR of reduced dilated convolutional network higher than dilated convolutional neural network 0.13dB and strand error smaller 0.07dB. PSNR of wide dilated convolutional network higher than dilated convolutional neural network 0.08dB and strand error smaller 0.09dB. Experiments also include difference scale of image super resolution and using difference types of data sets to test difference on the two proposed architectures. Final, proposed method applied to surveillance system. Results appoint image super resolution is able to enhance part of image features. In noise is improved, image texture isn’t blurry after image super resolution.
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Book chapters on the topic "Dilated convolution"

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Zhang, Jinglu, Yinyu Nie, Yao Lyu, Hailin Li, Jian Chang, Xiaosong Yang, and Jian Jun Zhang. "Symmetric Dilated Convolution for Surgical Gesture Recognition." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 409–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_39.

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Shen, Falong, and Gang Zeng. "Gaussian Dilated Convolution for Semantic Image Segmentation." In Advances in Multimedia Information Processing – PCM 2018, 324–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00776-8_30.

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Hu, Haigen, Chenghan Yu, Qianwei Zhou, Qiu Guan, and Qi Chen. "SAMDConv: Spatially Adaptive Multi-scale Dilated Convolution." In Pattern Recognition and Computer Vision, 460–72. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8543-2_37.

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Gupta, Sachin, Priya Goyal, Bhuman Vyas, Mohammad Shabaz, Suchitra Bala, and Aws Zuhair Sameen. "Dilated convolution model for lightweight neural network." In Next Generation Computing and Information Systems, 119–26. London: CRC Press, 2024. http://dx.doi.org/10.1201/9781003466383-20.

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Sun, Wei, Xijie Zhou, Xiaorui Zhang, and Xiaozheng He. "A Lightweight Neural Network Combining Dilated Convolution and Depthwise Separable Convolution." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 210–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48513-9_17.

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Qian, Likuan, Yuanfeng Lian, Qian Wei, Shuangyuan Wu, and Jianbin Zhang. "ODCN: Optimized Dilated Convolution Network for 3D Shape Segmentation." In Pattern Recognition and Computer Vision, 378–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31726-3_32.

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Wu, Yan, Wei Jiang, Jiqian Li, and Tao Yang. "Speeding Up Dilated Convolution Based Pedestrian Detection with Tensor Decomposition." In Intelligent Computing Methodologies, 117–27. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63315-2_11.

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Pan, Xiaoying, Dong Dai, Hongyu Wang, Xingxing Liu, and Weidong Bai. "Nasopharyngeal Organ Segmentation Algorithm Based on Dilated Convolution Feature Pyramid." In Lecture Notes in Electrical Engineering, 45–58. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6963-7_4.

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Wei, Xinlei, Yingji Liu, Wei Zhou, Haiying Xia, Daxin Tian, and Ruifen Cheng. "Traffic Crowd Congested Scene Recognition Based on Dilated Convolution Network." In Communications in Computer and Information Science, 134–46. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1160-5_12.

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Tureckova, Alzbeta, and Antonio J. Rodríguez-Sánchez. "ISLES Challenge: U-Shaped Convolution Neural Network with Dilated Convolution for 3D Stroke Lesion Segmentation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 319–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11723-8_32.

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Conference papers on the topic "Dilated convolution"

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Liu, Jen-Yu, and Yi-Hsuan Yang. "Dilated Convolution with Dilated GRU for Music Source Separation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/655.

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Stacked dilated convolutions used in Wavenet have been shown effective for generating high-quality audios. By replacing pooling/striding with dilation in convolution layers, they can preserve high-resolution information and still reach distant locations. Producing high-resolution predictions is also crucial in music source separation, whose goal is to separate different sound sources while maintain the quality of the separated sounds. Therefore, in this paper, we use stacked dilated convolutions as the backbone for music source separation. Although stacked dilated convolutions can reach wider context than standard convolutions do, their effective receptive fields are still fixed and might not be wide enough for complex music audio signals. To reach even further information at remote locations, we propose to combine a dilated convolution with a modified GRU called Dilated GRU to form a block. A Dilated GRU receives information from k-step before instead of the previous step for a fixed k. This modification allows a GRU unit to reach a location with fewer recurrent steps and run faster because it can execute in parallel partially. We show that the proposed model with a stack of such blocks performs equally well or better than the state-of-the-art for separating both vocals and accompaniment.
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Yang, Junyan, and Jie Jiang. "Dilated-CBAM: An Efficient Attention Network with Dilated Convolution." In 2021 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2021. http://dx.doi.org/10.1109/icus52573.2021.9641248.

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Highlander, Tyler, Bernard Abayowa, Mateen Rizki, and Hamilton Scott Clouse. "Conditional Dilated Convolution Attention Tracking Model." In 2019 Third IEEE International Conference on Robotic Computing (IRC). IEEE, 2019. http://dx.doi.org/10.1109/irc.2019.00096.

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Hua, Chen, Kuang Xu, and Tong Tong. "Crowd Counting with Dilated Inception Convolution." In ICCAI '21: 2021 7th International Conference on Computing and Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3467707.3467738.

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Wang, Xian, Lingqiao Liu, and Qinfeng Shi. "Enhancing Piano Transcription by Dilated Convolution." In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00224.

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Yin, Zhanhong, Renchao Qin, Chengzhuo Ye, Ya Li, Yaying He, Yue Shu, and Ruilin Jiang. "Dilated convolution based botnet detection model." In Third International Conference on Computer Communication and Network Security (CCNS 2022), edited by Chuanjun Zhao and Hilal Imane. SPIE, 2022. http://dx.doi.org/10.1117/12.2659107.

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G, Sakthi Priya, and Padmapriya N. "Texture Image Classification with Dilated Convolution Layers." In 2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). IEEE, 2023. http://dx.doi.org/10.1109/wispnet57748.2023.10133964.

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Pan, Shunan, Juan Du, Haonan Yu, Yuhan Cheng, Liye Mei, Chuan Xu, and Wei Yang. "Dilated Convolution Network for Road Damage Detection." In 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2023. http://dx.doi.org/10.1109/isctis58954.2023.10213051.

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Zhou, Shengwei, Caikou Chen, Guojiang Han, and Xielian Hou. "Deep Convolutional Neural Network with Dilated Convolution Using Small Size Dataset." In 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8865226.

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Wu, Lin (Yuanbo), Deyin Liu, Xiaojie Guo, Richang Hong, Liangchen Liu, and Rui Zhang. "Multi-scale Spatial Representation Learning via Recursive Hermite Polynomial Networks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/204.

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Abstract:
Multi-scale representation learning aims to leverage diverse features from different layers of Convolutional Neural Networks (CNNs) for boosting the feature robustness to scale variance. For dense prediction tasks, two key properties should be satisfied: the high spatial variance across convolutional layers, and the sub-scale granularity inside a convolutional layer for fine-grained features. To pursue the two properties, this paper proposes Recursive Hermite Polynomial Networks (RHP-Nets for short). The proposed RHP-Nets consist of two major components: 1) a dilated convolution to maintain the spatial resolution across layers, and 2) a family of Hermite polynomials over a subset of dilated grids, which recursively constructs sub-scale representations to avoid the artifacts caused by naively applying the dilation convolution. The resultant sub-scale granular features are fused via trainable Hermite coefficients to form the multi-resolution representations that can be fed into the next deeper layer, and thus allowing feature interchanging at all levels. Extensive experiments are conducted to demonstrate the efficacy of our design, and reveal its superiority over state-of-the-art alternatives on a variety of image recognition tasks. Besides, introspective studies are provided to further understand the properties of our method.
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