Therefore, in this work, we propose a Multimodal Variational Adversarial Active Learning (M-VAAL) method that uses auxiliary information from additional modalities to enhance the active sampling. We applied our
active learning algorithms, our approach is \textit{task agnostic}, i.e., it does not depend on the performance of the task for which we are trying to acquire labeled data. Our method learns a latent space using a variational autoencoder (VAE) and an adversarial network trained to ...
Unsupervised transfer autoencoder model based on adversarial strategy for non-linear process monitoring Control Engineering Practice (2024) J. Yu et al. Active features extracted by deep belief network for process monitoring ISA Transactions (2019) Y. Zhang et al. Modeling and monitoring of nonlinear...
Panel B is adapted with permission from So, S., Rho, J., Designing nanophotonic structures using conditional deep convolutional generative adversarial networks. Nanophotonics 8 (2019) 1255–1261, copyright 2019 CC BY 4.0. Show moreView chapter Review article Recent advances in describing and driving...
For image synthesis, both have upsides and downsides: GANs produce clearer images but, due to the adversarial tradeoffs between the two composite models, are unstable in training. VAEs are easier to train but, due to the nature of producing images from the “average” features of training dat...
近年,随着有监督学习的低枝果实被采摘的所剩无几,无监督学习成为了研究热点。VAE(Variational Auto-Encoder,变分自编码器)[1,2] 和GAN(Generative Adversarial Networks)等模型,受到越来越多的关注。 笔者最近也在学习 VAE 的知识(从深度学习角度)。首先,作为工程师,我想要正确的实现 VAE 算法,以及了解 VAE 能够...
A Variational Autoencoder is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this into a latent representation $z$, and a decoder, that takes a latent representation $z$ and returns a reconstruction $\hat{x}$. Infere...
Stochastic variational inequalities (SVI) and stochastic saddle-point (SSP) problems have become a central part of the modern machine learning toolbox. The main motivation behind this line of research is the design of algorithms for multiagent systems and adversarial training, which are more suitably...
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers, owing to the extremely high computational cost. Quantum computers promise a solution, although fault-
variational auto-encoder (VAE), generative adversarial network (GAN), attention mechanism, memory-augmented neural network, skip neural network, temporal difference VAE, stochastic neural network, stochastic temporal convolutional network, predicti... JT Chien - Wsdm 20: the Thirteenth Acm International...