Classify Images with Conceptor Network
This work addresses image classification for computer vision applications, but it appears incremental as it applies a known conceptor network to a standard task.
The paper tackled image classification by proposing a new conceptor network-based classifier, achieving superior results on MNIST, CIFAR-10, and CIFAR-100 datasets compared to conventional methods like Softmax Regression and SVM.
This article demonstrates a new conceptor network based classifier in classifying images. Mathematical descriptions and analysis are presented. Various tests are experimented using three benchmark datasets: MNIST, CIFAR-10 and CIFAR-100. The experiments displayed that conceptor network can offer superior results and flexible configurations than conventional classifiers such as Softmax Regression and Support Vector Machine (SVM).