Jan 4, 2024 · This paper introduces a modified variational autoencoder (VAEs) that contains an additional neural network branch.
Abstract– This paper introduces a modified variational autoencoder (VAEs) that contains an additional neural network branch. The resulting “branched VAE” ...
We will use a Variational Auto-encoder as a feature extraction tool and a logistic regressor to make the classification.
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Dual Branch Feature Representation and Variational Autoencoder for ...
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Sep 24, 2024 · In this article, we propose a DBFR-AENet for the multisource remote sensing image classification task.
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The method uses learning models such as Variational Autoencoder (VAE) to efficiently measure the differences between the test example and the training dataset.
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Jul 21, 2023 · In this paper we demonstrate one such machine learning workflow, Pair-Variational Autoencoders (PairVAE), that works with data from small-angle X-ray ...
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The down-sampling module is replaced by the Branch Convolutional Channel Attention Module (BCCAM), which employs a branching structure to enhance the model's ...
In many AE mod- els, one can often observe the label-encoder-decoder branch gives much better performance than feature-encoder-decoder branch. Imposing the VAE ...
Feb 8, 2024 · Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation.
In this paper, we propose a sequential variational autoencoder for video disentanglement, which is a representation learning method that can be used to ...