AIMC Topic: Learning

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Contrastive Adversarial Domain Adaptation Networks for Speaker Recognition.

IEEE transactions on neural networks and learning systems May 2, 2022
Domain adaptation aims to reduce the mismatch between the source and target domains. A domain adversarial network (DAN) has been recently proposed to incorporate adversarial learning into deep neural networks to create a domain-invariant space. Howev...
Neural Networks, Computer Learning Recognition, Psychology
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A Novel Transformer-Based Attention Network for Image Dehazing.

Sensors (Basel, Switzerland) Apr 30, 2022
Image dehazing is challenging due to the problem of ill-posed parameter estimation. Numerous prior-based and learning-based methods have achieved great success. However, most learning-based methods use the changes and connections between scale and de...
Humans Neural Networks, Computer Learning Disease Progression Image Processing, Computer-Assisted
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Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network.

Computational intelligence and neuroscience Apr 28, 2022
With the continuous development of imaging sensors, images contain more and more information, the images presented by different types of sensors are different, and the images obtained by the same type of sensors under different parameters or conditio...
Neural Networks, Computer Algorithms Learning Image Processing, Computer-Assisted Artifacts
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Deep networks may capture biological behavior for shallow, but not deep, empirical characterizations.

Neural networks : the official journal of the International Neural Network Society Apr 27, 2022
We assess whether deep convolutional networks (DCN) can account for a most fundamental property of human vision: detection/discrimination of elementary image elements (bars) at different contrast levels. The human visual process can be characterized ...
Humans Neural Networks, Computer Learning
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DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network.

Sensors (Basel, Switzerland) Apr 26, 2022
Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated by smart sensors of user devices, where a central parameter server (PS) coordinates multiple user devices to train a global model. However, relying on central...
Machine Learning Learning Communication Consensus Privacy
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Chalcogenide optomemristors for multi-factor neuromorphic computation.

Nature communications Apr 26, 2022
Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase...
Neural Networks, Computer Learning Neurons Electronics Synapses
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Learning aerodynamics with neural network.

Scientific reports Apr 26, 2022
We propose a neural network (NN) architecture, the Element Spatial Convolution Neural Network (ESCNN), towards the airfoil lift coefficient prediction task. The ESCNN outperforms existing state-of-the-art NNs in terms of prediction accuracy, with two...
Neural Networks, Computer Learning
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Design and Implementation of Tourism Teaching System Based on Artificial Intelligence Technology.

Computational intelligence and neuroscience Apr 23, 2022
The tourism teaching system provides all kinds of teaching resources for students and shares good teachers, which greatly improves the quality of teaching and learning, and it enables students to teach and learn randomly in the system. The mode of ed...
Humans Artificial Intelligence Technology Learning Students Tourism
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Training of artificial neural networks with the multi-population based artifical bee colony algorithm.

Network (Bristol, England) Apr 21, 2022
Nowadays, artificial intelligence has gained recognition in every aspect of life. Artificial neural networks, one of the most efficient artificial intelligence techniques, is remarkably successful in computers' acquisition of the learning and interpr...
Humans Neural Networks, Computer Artificial Intelligence Algorithms Learning
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Attributed graph clustering with multi-task embedding learning.

Neural networks : the official journal of the International Neural Network Society Apr 20, 2022
Attributed graph clustering is challenging as it needs to effectively combine both graph structure and node feature information to accomplish node clustering. Recent studies mostly adopt graph neural networks to learn node embeddings, then apply trad...
Neural Networks, Computer Learning Cluster Analysis
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