convolutional neural network (cnn)

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Top Papers in Convolutional neural network (cnn)

Computer Vision

Graphics

Artificial Intelligence

Assessing unconstrained surgical cuttings in VR using CNNs

We present a Convolutional Neural Network (CNN) suitable to assess unconstrained surgical cuttings, trained on a dataset created with a data augmentation technique.

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Hardware Architecture

Machine Learning

Computer Vision

Distributed

Implementations of fundamental neural network layers on the Manticore cluster-based many-core architecture

Implementing CNN Layers on the Manticore Cluster-Based Many-Core Architecture

This document presents implementations of fundamental convolutional neuralnetwork (cnn) layers on the manticore cluster-based many-core architecture anddiscusses their characteristics and trade-offs.

Convolutional neuralnetwork (cnn), many-core architecture, many-core architecture, manticore cluster-based many-core architecture.

Machine Learning

Computer Vision

Stats Machine Learning

JigsawHSI: a network for Hyperspectral Image classification

This article describes the performance of JigsawHSI,a convolutional neural network (CNN) based on Inception but tailored for geoscientific analyses, on classification with the Indian Pines, Pavia Univ

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Computer Vision

ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images

Semantic segmentation of remotely sensed images plays a crucial role in precision agriculture, environmental protection, and economic assessment. In recent years, substantial fine-resolution remote se

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Computer Vision

Interpretable Compositional Convolutional Neural Networks

The reasonable definition of semantic interpretability presents the core challenge in explainable AI. This paper proposes a method to modify a traditional convolutional neural network (CNN) into an in

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Computer Vision

Image and Video Processing

Deep learning based classification of lung cancer images

Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)

Artificial Intelligence

Cryptography and Security

R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections

The influence of Deep Learning on image identification and natural language processing has attracted enormous attention globally. The convolution neural network that can learn without prior extraction

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Computer Vision

Image and Video Processing

Perineural Invasion Detection in Multiple Organ Cancer Based on Deep Convolutional Neural Network

Perineural invasion (PNI) by malignant tumor cells has been reported as an independent indicator of poor prognosis in various cancers. Assessment of PNI in small nerves on glass slides is a labor-inte

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Computer Vision

Image and Video Processing

Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism

Objectives. The aim of this study was to investigate whether a deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs. Study Design. A datase

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Computer Vision

Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features

Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly. However, the point cloud is sparse, unst

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Computer Vision

Learning about Discriminative Object Shape in Contextual Neural Networks

Shape or Texture: Understanding Discriminative Features in CNNs

Recent studies have shown that convolutional neural networks (cnns) actually exhibit a ` texture bias' : given an image with both texture and shape cues (e.g., a stylized image), a cnn is biasedtowards predicting the category corresponding to the texture.

However, these previous studies conduct experiments on the final classification output of the network, and fail to robustly evaluate the bias contained (i) in the latent representations, and (ii) on a per-pixel level.

Computer Vision

Is Each Layer Non-trivial in CNN?

Convolutional neural network (CNN) models have achieved great success in many fields. With the advent of ResNet, networks used in practice are getting deeper and wider. However, is each layer non-triv

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Machine Learning

Computer Vision

Ubicomp Digital 2020 -- Handwriting classification using a convolutional recurrent network

The Ubicomp Digital 2020 -- Time Series Classification Challenge from STABILO is a challenge about multi-variate time series classification. The data collected from 100 volunteer writers, and contains

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Computer Vision

Image and Video Processing

Deep Convolutional Neural Network for Non-rigid Image Registration

Images taken at different times or positions undergo transformations such as rotation, scaling, skewing, and more. The process of aligning different images which have undergone transformations can be

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Computer Vision

Artificial Intelligence

DA$^{\textbf{2}}$-Net : Diverse & Adaptive Attention Convolutional Neural Network

Standard Convolutional Neural Network (CNN) designs rarely focus on the importance of explicitly capturing diverse features to enhance the network's performance. Instead, most existing methods follow

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Machine Learning

Computer Vision

Cryptography and Security

MissMarple : A Novel Socio-inspired Feature-transfer Learning Deep Network for Image Splicing Detection

In this paper we propose a novel socio-inspired convolutional neural network (CNN) deep learning model for image splicing detection. Based on the premise that learning from the detection of coarsely s

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Machine Learning

Materials Science

Disordered Systems and Neural Networks

Determination of the Interface between Amorphous Insulator and Crystalline 4H-SiC in Transmission Electron Microscope Image by using Convolutional Neural Network

A rough interface seems to be one of the possible reasons for low channel mobility (conductivity) in SiC MOSFETs. To evaluate the mobility by interface roughness, we drew a boundary line between amorp

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Computer Vision

Artificial Intelligence

Building a symbiosis between aal neural networks and recurrent neural networks to recognize cultural/anthropological Italian sign language gestures

Dynamic Gesture Recognition

This project aims to builda symbiosis between a convolutional neural network (cnn)[1]and a recurrent neural network (rnn)to builda symbiosis between a convolutional neural network (cnn)[1]and a recurrent neural network (rnn)to recognizecultural/anthropological italian sign language gestures fromvideos.

The cnn extracts important features that later areused by the rnn.with the help of temporalinformation we are able to store temporalinformation inside the model to provide contextualinformationfrom previous frames to enhance the prediction accuracy.

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