Convolutional neural network ≈ Convolutional neural network
View article: ImageNet classification with deep convolutional neural networks
ImageNet classification with deep convolutional neural networks Open
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%…
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MizAR 60 for Mizar 50 Open
As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60% of the Mizar theorems in the hammer setting. We also automatically prove 75% of the Mizar theorems when the automated provers are…
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Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories Open
Functionality-specific vulnerabilities, which mainly occur in Application Programming Interfaces (APIs) with specific functionalities, are crucial for software developers to detect and avoid. When detecting individual functionality-specifi…
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A survey on Image Data Augmentation for Deep Learning Open
Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a fun…
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Open
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks…
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Semi-Supervised Classification with Graph Convolutional Networks Open
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional archi…
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Attention Is All You Need Open
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. …
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Dynamic Graph CNN for Learning on Point Clouds Open
Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have lon…
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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning Open
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchica…
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Learning on Graphs (NTDS'18) Open
Lecture at the EPFL master course A Network Tour of Data Science.
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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Open
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that care…
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Graph neural networks: A review of methods and applications Open
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a mode…
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Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition Open
Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power …
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Attention U-Net: Learning Where to Look for the Pancreas Open
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image…
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Convolutional neural networks: an overview and application in radiology Open
Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically …
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Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation Open
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with fil…
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An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling Open
For most deep learning practitioners, sequence modeling is synonymous with recurrent networks. Yet recent results indicate that convolutional architectures can outperform recurrent networks on tasks such as audio synthesis and machine tran…
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A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects Open
A convolutional neural network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it…
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Using Deep Learning for Image-Based Plant Disease Detection Open
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and…
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TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Open
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net…
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UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation Open
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extens…
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R-FCN: Object Detection via Region-based Fully Convolutional Networks Open
We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our re…
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation Open
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current n…
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Deep Learning for Computer Vision: A Brief Review Open
Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief…
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Res2Net: A New Multi-Scale Backbone Architecture Open
Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to con…
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Deep learning with convolutional neural networks for EEG decoding and visualization Open
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG an…
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COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images Open
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with …
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Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? Open
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a …
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SECOND: Sparsely Embedded Convolutional Detection Open
LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when …
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Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks Open
Over the last decade, Convolutional Neural Network (CNN) models have been\nhighly successful in solving complex vision problems. However, these deep\nmodels are perceived as "black box" methods considering the lack of\nunderstanding of the…