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PCAAE: Principal Component Analysis Autoencoder for organising the latent space of generative networks
Autoencoders and generative models produce some of the most spectacular ...
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CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images
Ambiguity is inevitable in medical images, which often results in differ...
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A PCA-like Autoencoder
An autoencoder is a neural network which data projects to and from a low...
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Modal Uncertainty Estimation via Discrete Latent Representation
Many important problems in the real world don't have unique solutions. I...
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A Forest from the Trees: Generation through Neighborhoods
In this work, we propose to learn a generative model using both learned ...
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Conditional generation of multi-modal data using constrained embedding space mapping
We present a conditional generative model that maps low-dimensional embe...
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On latent position inference from doubly stochastic messaging activities
We model messaging activities as a hierarchical doubly stochastic point ...
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Rate-Distortion Optimization Guided Autoencoder for Generative Approach with quantitatively measurable latent space
In the generative model approach of machine learning, it is essential to acquire an accurate probabilistic model and compress the dimension of data for easy treatment. However, in the conventional deep-autoencoder based generative model such as VAE, the probability of the real space cannot be obtained correctly from that of in the latent space, because the scaling between both spaces is not controlled. This has also been an obstacle to quantifying the impact of the variation of latent variables on data. In this paper, we propose Rate-Distortion Optimization guided autoencoder, in which the Jacobi matrix from real space to latent space has orthonormality. It is proved theoretically and experimentally that (i) the probability distribution of the latent space obtained by this model is proportional to the probability distribution of the real space because Jacobian between two spaces is constant; (ii) our model behaves as non-linear PCA, where energy of acquired latent space is concentrated on several principal components and the influence of each component can be evaluated quantitatively. Furthermore, to verify the usefulness on the practical application, we evaluate its performance in unsupervised anomaly detection and it outperforms current state-of-the-art methods.
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