Variational autoencoderOne-class classificationVoice activity detectionDetecting human speech is foundational for a wide range of emerging intelligent applications. However, accurately detecting human speech is challenging, especially in the presence of unknown noise patterns. Generally, deep learning-based ...
Official PyTorch implementation of 🏁 MFCVAE 🏁: "Multi-Facet Clustering Variatonal Autoencoders (MFCVAE)" (NeurIPS 2021). A class of variational autoencoders to find multiple disentangled clusterings of data. - GitHub - FabianFalck/mfcvae: Official
A generative model for zero shot learn- ing using conditional variational autoencoders. In CVPR- Workshops, pages 2188–2196, 2018. [25] Ashish Mishra, Anubha Pandey, and Hema A Murthy. Zero- shot learning for action recognition using synthesized fea- tures. Neurocomputing,...
VAE based explainer Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions IEEE Segmentation based explanation Deep Co-Attention Network for Multi-View Subspace Learning Arxiv PyTorch Integrated CAM INTEGRATED GRAD-CAM: SENSITIVITY-AWARE VISUAL EXPLANAT...
Machine learning models used for classification are typically designed with the assumption of an equal number of instances for each class. In CI datasets, machine learning models tend to be more biased towards the majority class, causing improper classification of the minority class and leading to ...
This work proposes an extension to the classic GAN framework that includes a variational autoencoder (VAE) and an external memory mechanism to overcome these limitations and generate synthetic data accurately describing imbalanced class distributions commonly found in clinical variables. Methods: The ...
we propose a supervised autoencoder with an intermediate embedding model to disperse the labeled latent vectors. With the enhanced autoencoder initialization, we also build an architecture of BAGAN with gradient penalty (BAGAN-GP). Our proposed model overcomes the unstable issue in original BAGAN and...
有三种解决方法:(1)输出用混合高斯来表述,但是需要每次都要根据输出个数调整高斯的个数;(2)Latent variable models是在输入的时候加入一个高斯干扰,这样尽管输出是一个高斯分布,仍旧可以区分multimodal ,但是很难训练学习(问题:如何变的?)例子有conditional variational autoencoder,stein variational gradient descent ;...
Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural network 2019, Sensors (Switzerland) TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System 2019, IEEE Access TSDL: A Two-Stage ...
[48] propose an unsupervised modeling approach for novelty detection based on Deep Gaussian Processes (DGP) in autoencoder configuration. The proposed DGP autoencoder is trained by approximating the DGP layers using random feature expansions, and by conducting stochastic variational inference on the ...