multi-view-AE is a collection of multi-modal autoencoder models for learning joint representations from multiple modalities of data. The package is structured such that all models have fit, …

This library offers Python implementations of numerous multi-view autoencoder models, presented within a user-friendly framework. Through benchmarking experiments, we evaluate …

Jan 1, 2025 · Our novel Elastic Deep Multi-view Autoencoder with Diversity Embedding (EDMVAE-DE) method incorporates an Elastic Deep AE to enhance its robustness.

Feb 1, 2024 · To discover the essential geometry information of multi-view data from each single view, in this paper, we propose a novel hierarchical graph augmented stacked autoencoder …

Jan 29, 2024 · In this paper, we present a method for self-supervised learning from synchronized multi-view videos. We use a cross-view reconstruction task to inject geometry information in …

Learning a comprehensive representation from multiview data is crucial in many real-world applications. Multiview representation learning (MRL) based on nonnega.

Feb 1, 2023 · To this end, we present the Multi-View Masked Autoencoder (MV-MAE), a simple and scalable framework for multi-view representation learning. Our main idea is to mask …

By far the most popular group of multi-view autoencoder models are multi-view extensions of Variational Autoencoders (VAEs) where the latent space is regularised by mapping the …

multi-view-AE is a collection of multi-modal autoencoder models for learning joint representations from multiple modalities of data. The package is structured such that all models have fit, …

May 25, 2023 · This paper introduces a novel approach, reconstructed graph constrained auto-encoders (RGCAE), for multi-view representation learning. Unlike existing methods, our …