AIMC Topic: Learning

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A Brain-Inspired Approach for Collision-Free Movement Planning in the Small Operational Space.

IEEE transactions on neural networks and learning systems
In a small operational space, e.g., mesoscale or microscale, we need to control movements carefully because of fragile objects. This article proposes a novel structure based on spiking neural networks to imitate the joint function of multiple brain r...

Memory Recall: A Simple Neural Network Training Framework Against Catastrophic Forgetting.

IEEE transactions on neural networks and learning systems
It is widely acknowledged that biological intelligence is capable of learning continually without forgetting previously learned skills. Unfortunately, it has been widely observed that many artificial intelligence techniques, especially (deep) neural ...

Robust Facial Landmark Detection by Multiorder Multiconstraint Deep Networks.

IEEE transactions on neural networks and learning systems
Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facial landmark detection methods neglect to explore the high-order feature...

Contrastive Adversarial Domain Adaptation Networks for Speaker Recognition.

IEEE transactions on neural networks and learning systems
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...

A Novel Transformer-Based Attention Network for Image Dehazing.

Sensors (Basel, Switzerland)
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...

Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network.

Computational intelligence and neuroscience
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...

Deep networks may capture biological behavior for shallow, but not deep, empirical characterizations.

Neural networks : the official journal of the International Neural Network Society
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 ...

DACFL: Dynamic Average Consensus-Based Federated Learning in Decentralized Sensors Network.

Sensors (Basel, Switzerland)
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...

Chalcogenide optomemristors for multi-factor neuromorphic computation.

Nature communications
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...

Learning aerodynamics with neural network.

Scientific reports
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...