Oct 17, 2022 · We in this paper propose a novel generative modeling paradigm by introducing a Bayesian network-based regularize on a cascade Variational Auto-Encoder (VAE).
Hence, we in this paper propose a novel generative modeling paradigm by introducing a Bayesian network-based regularizer on a cascade Variational Auto-Encoder.
Hence, we in this paper propose a novel generative modeling paradigm by introducing a Bayesian network-based regularizer on a cascade Variational Auto-Encoder.
The VAE consists of two networks: an encoder that maps an input image sample x to a latent space representation z, and a decoder that reconstructs the sample in ...
Oct 17, 2022 · "Cascade Variational Auto-Encoder for Hierarchical Disentanglement". Proceedings of the 31st ACM International Conference on Information & ...
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One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity.
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 present a novel framework for disentangled representation learning, DeVAE, which utilizes hi- erarchical latent spaces with decreasing ...
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models. - matthewvowels1/Awesome-VAEs.
Feb 1, 2023 · Our work proposes a novel decremental variational autoencoder with hierarchy latent spaces to optimize multiple objectives in these layers, and ...