AIMC Topic: Pattern Recognition, Visual

Clear Filters Showing 91 to 100 of 152 articles

Learning from label proportions on high-dimensional data.

Neural networks : the official journal of the International Neural Network Society
Learning from label proportions (LLP), in which the training data is in the form of bags and only the proportion of each class in each bag is available, has attracted wide interest in machine learning. However, how to solve high-dimensional LLP probl...

Accelerated low-rank representation for subspace clustering and semi-supervised classification on large-scale data.

Neural networks : the official journal of the International Neural Network Society
The scalability of low-rank representation (LRR) to large-scale data is still a major research issue, because it is extremely time-consuming to solve singular value decomposition (SVD) in each optimization iteration especially for large matrices. Sev...

Coupled generative adversarial stacked Auto-encoder: CoGASA.

Neural networks : the official journal of the International Neural Network Society
Coupled Generative Adversarial Network (CoGAN) was recently introduced in order to model a joint distribution of a multi modal dataset. The CoGAN model lacks the capability to handle noisy data as well as it is computationally expensive and inefficie...

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods.

Neural networks : the official journal of the International Neural Network Society
This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Con...

A border-ownership model based on computational electromagnetism.

Neural networks : the official journal of the International Neural Network Society
The mathematical relation between a vector electric field and its corresponding scalar potential field is useful to formulate computational problems of lower/middle-order visual processing, specifically related to the assignment of borders to the sid...

STDP-based spiking deep convolutional neural networks for object recognition.

Neural networks : the official journal of the International Neural Network Society
Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively sha...

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

PLoS computational biology
All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here...

Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task.

PloS one
Emotions play a significant role in internal regulatory processes. In this paper, we advocate four key ideas. First, novelty detection can be grounded in the sensorimotor experience and allow higher order appraisal. Second, cognitive processes, such ...

Lifelong learning of human actions with deep neural network self-organization.

Neural networks : the official journal of the International Neural Network Society
Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather l...

Robust Alternating Low-Rank Representation by joint L- and L-norm minimization.

Neural networks : the official journal of the International Neural Network Society
We propose a robust Alternating Low-Rank Representation (ALRR) model formed by an alternating forward-backward representation process. For forward representation, ALRR first recovers the low-rank PCs and random corruptions by an adaptive local Robust...