AutoencoderSelf-supervisedCross-modal retrieval has gained lots of attention in the era of the multimedia data explosion. Taking advantage of low storage cost and fast retrieval speed, hash learning-based methods become more and more popular in this field. The crucial bottlenecks of cross-modal ...
However, these methods are mostly supervised. In practical applications, annotating large amounts of data is a very time-consuming and laborious task. Furthermore, efficiently using a large amount of unlabeled data for hash learning is challenging. In this paper, we create a new autoencoder ...
Vibration-based Structural Health Monitoring (SHM) approaches primarily fall into two categories: model-based and data-driven methods. Model-based methods, though accurate, are computationally intensive, making them less feasible for widespread SHM applications [5]. In contrast, data-driven methods leve...
Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and cluster...
The network contains one convolution layer in the encoder and one transpose convolution layer in the decoder, responsible for the input image compression and the stored data decompression, respectively (see Methods). The inputted high-resolution test image needs to be segmented into a series of sub...
Community detection is a challenging issue because most existing methods are not well suited for complex social networks with ambiguous structures. In this paper, we propose a novel community detection method named Stacked Autoencoder-Based Community Detection Method via Ensemble Clustering (CDMEC). Thi...
L1 regularization and KL-divergence are usually the two methods that are taken into consideration while creating a sparsity penalty. In the first case (4) for sparse autoencoders becomes (5)Ls=argminA,BE[Δ(x,xˆ)]+λ∑iai,where ai is the activation at the ith hidden layer and i ...
have also given rise to challenges in simulation and optimization of the diverse machine topologies involved. One core challenge is to deal with the complexities inherent to magneto-static finite element analysis (FEA). Traditional FEA methods are notably resource-intensive and time-consuming, constrain...
how to make the high-dimensional network represented in low-dimensional vector space through network becomes an important issue. The typical algorithms of current autoencoder-based network embedding methods include DNGR and SDNE. DNGR method trains the Positive Pointwise Mutual Information (PPMI) matrix...
Browse State-of-the-Art Datasets Methods More Sign In Convolutional autoencoder-based multimodal one-class classification 25 Sep 2023 · Firas Laakom, Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis, Moncef Gabbouj · Edit social preview ...