Variational Adversarial Active LearningSamarth Sinha *University of Torontosamarth.sinha@mail.utoronto.caSayna Ebrahimi *UC Berkeleysayna@eecs.berkeley.eduTrevor DarrellUC Berkeleytrevor@eecs.berkeley.eduAbstractActive learning aims to develop label-eff i cient algo-rithms by sampling the most representa...
Active learning ... K Kim,D Park,KI Kim,... - Computer Vision & Pattern Recognition 被引量: 0发表: 2021年 Task-Aware Variational Adversarial Active Learning Deep learning has achieved remarkable performance in various tasks thanks to massive labeled datasets. However, there are often cases ...
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 method to two datasets: i) brain tumor segmentation and multi-label classification using ...
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 ...
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 chapterExplore book 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...
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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...
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