Extended Relational Autoencoders for Feature Extraction in CBIRBashir, M. K.Saleem, Y.Naseer, S.Technical Journal of University of Engineering & Technology Taxila
RandNet [89] is an unsupervised error detection technique that uses a randomly connected autoencoder. It incorporates different structures and connection densities as base components and combines adaptive sampling techniques to enhance the model’s efficiency and effectiveness. It first uses neural network...
Whereas most previous approaches use a single, real-valued vector \(e_i\) for every \(v_i \in \mathcal {V}\) optimized directly in training, we compute representations through an R-GCN encoder with \(e_i = h_i^{(L)}\), similar to the graph auto-encoder model introduced in [...
These models included Support Vector Machines (SVM), Isolation Forest (IM), Principal Component Analysis (PCA), Neural Networks (NN), Autoencoders (AE), Variational Autoencoders (VAE), and Recurrent Neural Networks (RNN). Each model was chosen based on its popularity and effectiveness in ...