NCP-VAE: Variational Autoencoders with Noise Contrastive Priors
By Jyoti Aneja, Alexander Gerhard Schwing, Jan Kautz, Arash Vahdat. 2020
37 citations recorded by the source corpus.
Subfields: Generative Models, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Generative Models
- Arash Vahdat
- Karsten Kreis
- Jiaming Song
- Jan Kautz
- Stefano Ermon
- Chenlin Meng
- Sanja Fidler
- Abhishek Sinha
In Probabilistic & Bayesian Methods
- Chenlin Meng
- Jiaming Song
- Arash Vahdat
- Stefano Ermon
- Karsten Kreis
- Jan Kautz
- Abhishek Sinha
- Sanja Fidler
Citations in the corpus
- D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation (2021, methodology): “Addressing posterior mismatch in VAEs Most methods address this mismatch problem by improving inference models [64, 51, 89], prior models [90, 3, 87], or objective functions [103, 104, 105, 1, 59]; all these approaches optimize the posterior model to be close to the prior.”
- Score-based Generative Modeling in Latent Space (2021, methodology): “Furthermore, classifiers [54, 55, 56], adversarial methods [57], and other techniques [58, 59] have been used to define prior distributions implicitly.”
- Symbolic Music Generation with Diffusion Models (2021, neutral): “strain the VAE’s prior before sampling (Engel et al., 2017a; Aneja et al., 2020).”
- Taming Transformers for High-Resolution Image Synthesis (2020, neutral): “CelebAHQ results reproduced from [1, 47, 67, 22], FFHQ from [58, 28].”
- LION: Latent Point Diffusion Models for 3D Shape Generation (2022, background): “[76] Jyoti Aneja, Alexander Schwing, Jan Kautz, and Arash Vahdat.”