AIMC Topic: Pattern Recognition, Automated

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SPLASH: Learnable activation functions for improving accuracy and adversarial robustness.

Neural networks : the official journal of the International Neural Network Society
We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to adversarial attacks. SPLASH units have both a simple parameterization a...

AR3D: Attention Residual 3D Network for Human Action Recognition.

Sensors (Basel, Switzerland)
At present, in the field of video-based human action recognition, deep neural networks are mainly divided into two branches: the 2D convolutional neural network (CNN) and 3D CNN. However, 2D CNN's temporal and spatial feature extraction processes are...

Knowledge graph embedding with shared latent semantic units.

Neural networks : the official journal of the International Neural Network Society
Knowledge graph embedding (KGE) aims to project both entities and relations into a continuous low-dimensional space. However, for a given knowledge graph (KG), only a small number of entities and relations occur many times, while the vast majority of...

A fast saddle-point dynamical system approach to robust deep learning.

Neural networks : the official journal of the International Neural Network Society
Recent focus on robustness to adversarial attacks for deep neural networks produced a large variety of algorithms for training robust models. Most of the effective algorithms involve solving the min-max optimization problem for training robust models...

Dense Residual Network: Enhancing global dense feature flow for character recognition.

Neural networks : the official journal of the International Neural Network Society
Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply s...

Visual question answering based on local-scene-aware referring expression generation.

Neural networks : the official journal of the International Neural Network Society
Visual question answering requires a deep understanding of both images and natural language. However, most methods mainly focus on visual concept; such as the relationships between various objects. The limited use of object categories combined with t...

Synchronization criteria of delayed inertial neural networks with generally Markovian jumping.

Neural networks : the official journal of the International Neural Network Society
In this paper, the synchronization problem of inertial neural networks with time-varying delays and generally Markovian jumping is investigated. The second order differential equations are transformed into the first-order differential equations by ut...

Intraoral radiograph anatomical region classification using neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Dental radiography represents 13% of all radiological diagnostic imaging. Eliminating the need for manual classification of digital intraoral radiographs could be especially impactful in terms of time savings and metadata quality. However, a...

Multi-Path and Group-Loss-Based Network for Speech Emotion Recognition in Multi-Domain Datasets.

Sensors (Basel, Switzerland)
Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labe...

End-to-end novel visual categories learning via auxiliary self-supervision.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most of the methods have adopted a common assumption that there is always labeled data from the same class of unlabeled data,...