منابع مشابه
Denoising Adversarial Autoencoders
Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a form of generative model, may be trained by learning to reconstruct unlabelled input data from a latent representation space. More robust representations may be produced by an autoencoder if it learns to recover clea...
متن کاملAdversarial Autoencoders
In this paper, we propose the “adversarial autoencoder” (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution. Matching the aggregated posterior to the prior ensures that generating fro...
متن کاملMultimodal Stacked Denoising Autoencoders
We propose a Multimodal Stacked Denoising Autoencoder for learning a joint model of data that consists of multiple modalities. The model is used to extract a joint representation that fuses modalities together. We have found that this representation is useful for classification tasks. Our model is made up of layers of denoising autoencoders which are trained locally to denoise corrupted version...
متن کاملMarginalized Stacked Denoising Autoencoders
Stacked Denoising Autoencoders (SDAs) [4] have been used successfully in many learning scenarios and application domains. In short, denoising autoencoders (DAs) train one-layer neural networks to reconstruct input data from partial random corruption. The denoisers are then stacked into deep learning architectures where the weights are fine-tuned with back-propagation. Alternatively, the outputs...
متن کاملComposite Denoising Autoencoders
In representation learning, it is often desirable to learn features at different levels of scale. For example, in image data, some edges will span only a few pixels, whereas others will span a large portion of the image. We introduce an unsupervised representation learning method called a composite denoising autoencoder (CDA) to address this. We exploit the observation from previous work that i...
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2019
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2018.2852738