AIMC Topic: Neural Networks, Computer

Clear Filters Showing 11361 to 11370 of 31376 articles

Survey and Evaluation of Neural 3D Shape Classification Approaches.

IEEE transactions on pattern analysis and machine intelligence
Classification of 3D objects - the selection of a category in which each object belongs - is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture ...

Source Data-Absent Unsupervised Domain Adaptation Through Hypothesis Transfer and Labeling Transfer.

IEEE transactions on pattern analysis and machine intelligence
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to the source data, and thus are not applicable when th...

GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention.

IEEE transactions on pattern analysis and machine intelligence
Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention mo...

Detailed Avatar Recovery From Single Image.

IEEE transactions on pattern analysis and machine intelligence
This paper presents a novel framework to recover detailed avatar from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, texture, and viewpoints. Prior methods typically attempt to recover the huma...

Learning to Forget for Meta-Learning via Task-and-Layer-Wise Attenuation.

IEEE transactions on pattern analysis and machine intelligence
Few-shot learning is an emerging yet challenging problem in which the goal is to achieve generalization from only few examples. Meta-learning tackles few-shot learning via the learning of prior knowledge shared across tasks and using it to learn new ...

Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation.

IEEE transactions on pattern analysis and machine intelligence
Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bound...

Organizing Reliable Polymer Electrode Lines in Flexible Neural Networks via Coffee Ring-Free Micromolding in Capillaries.

ACS applied materials & interfaces
With an increase in the demand for smart wearable systems, artificial synapse arrays for flexible neural networks have received considerable attention. A synaptic device with a two-terminal configuration is promising for complex neural networks becau...

A Novel Image-Based Diagnosis Method Using Improved DCGAN for Rotating Machinery.

Sensors (Basel, Switzerland)
Rotating machinery plays an important role in industrial systems, and faults in the machinery may damage the system health. A novel image-based diagnosis method using improved deep convolutional generative adversarial networks (DCGAN) is proposed for...

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network.

Sensors (Basel, Switzerland)
As the aggravation of road congestion leads to frequent traffic crashes, it is necessary to relieve traffic pressure through traffic flow prediction. As well, the traffic flow of the target road section to be predicted is also closely related to the ...

Recurrent neural networks with explicit representation of dynamic latent variables can mimic behavioral patterns in a physical inference task.

Nature communications
Primates can richly parse sensory inputs to infer latent information. This ability is hypothesized to rely on establishing mental models of the external world and running mental simulations of those models. However, evidence supporting this hypothesi...