Convolutional Neural Networks (ConvNets) have been candidly deployed in the scope of computer vision and related fields. Nevertheless, the dynamics of training of these neural networks lie still elusive: it is hard and computationally expensive to train them. A myriad of architectures and training strategies have been proposed to overcome this challenge and address several problems in image processing such as speech, image and action recognition as well as object detection. In this article, we propose a novel Particle Swarm Optimization (PSO) based training for ConvNets. In such framework, the vector of weights of each ConvNet is typically cast as the position of a particle in phase space whereby PSO collaborative dynamics intertwines with Stochastic Gradient Descent (SGD) in order to boost training performance and generalization. Our approach goes as follows: i) [warm-up phase] each ConvNet is trained independently via SGD; ii) [collaborative phase] ConvNets share among themselves their current vector of weights (or particle-position) along with their gradient estimates of the Loss function. Distinct step sizes are coined by distinct ConvNets. By properly blending ConvNets with large (possibly random) step-sizes along with more conservative ones, we propose an algorithm with competitive performance with respect to other PSO-based approaches on Cifar-10 (accuracy of 98.31%). These accuracy levels are obtained by resorting to only four ConvNets -- such results are expected to scale with the number of collaborative ConvNets accordingly. We make our source codes available for download https://github.com/leonlha/PSO-ConvNet-Dynamics.

翻译：然而,这些神经网络的培训动态仍然难以捉摸:培训这些神经网络很难,而且计算成本昂贵。为了克服这一挑战,并解决图像处理方面的诸多问题,例如语音、图像和动作识别以及目标检测。在本篇文章中,我们提议为CONNet提供基于Conval Informization(PSO)的新版本的Particle Swarm Oppimization (PSO) 培训。在这个框架中,每个ConvNet的重量矢量通常作为阶段空间的粒子定位,即PSO与Stochatical Gradentire(SGD)的合作动态互动,以提高培训绩效和总体化。我们的方法如下:i)通过SGD(SGD)独立培训每个ConvNet;ii)[腐蚀性阶段]ConvildNet在它们自己中间分享其当前重量矢量(或粒子定位),以及它们对于损失的梯度估计值,在Sloverial-comal recal recal 功能上,Pral-deal-dealal lax a mission lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax lax labs lax lax lades lades lax lax lax lades lades lax lades lades laut lades lax lax lax lax lax lax lax lax lax lax lauts lax laut lax laut lauts lauts lauts lauts lax lauts lax lauts lax lauts lauts lax lax lax lax lauts lax lax lax lax lax lax lax lax lax lax lacal lax lax lax lax lax