This paper proposes a novel hybrid model that synergistically integrates Variational Autoencoders (VAEs) and Speeded-Up Robust Features (SURF) to address these challenges. The VAE component captures ...

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different ...

One popular technique in this field is deep variational autoencoders (VAEs), which combine ... the work exploits deep VAEs by leveraging the latent space of a deterministic autoencoder (DAE). This ...

Finally, a separate classifier is trained to predict mTBI or normal ... The considered models are a simple autoencoder (AE), a variational autoencoder (VAE), a supervised VAE (SVAE), and an ...

A desirable feature of a variational autoencoder is the existence of an interpretable ... and ECG-specific), we train a logistic regression classifier to identify subjects with cardiac disease. Figure ...

LSSAE is a VAE-based probabilistic framework which incorporates variational inference to identify the continuous ... Specify the feature extractor and classifier --model-func MNIST_CNN --cla-func ...

Through jointly training a variational autoencoder and a deep neural networks classifier, we convert the original entangled raw data into latent variables with Gaussian probabilistic distributions in ...

Abstract: This paper proposes a non-parallel voice conversion (VC) method using a variant of the conditional variational autoencoder (VAE) called an auxiliary classifier VAE. The proposed method has ...