Network-to-Network Translation with Conditional Invertible Neural Networks
By Robin Rombach, Patrick Esser, Björn Ommer. 2020
48 citations recorded by the source corpus.
Subfields: Model Architectures
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Model Architectures
- Björn Ommer
- Robin Rombach
- Andreas Blattmann
- Patrick Esser
- Andreas R. Dengel
- Maosong Sun
- Michael Dorkenwald
- Dominik Lorenz
Citations in the corpus
- High-Resolution Image Synthesis with Latent Diffusion Models (2021, methodology): “Two-Stage Image Synthesis To mitigate the shortcomings of individual generative approaches, a lot of research [11,23,65,68,97,99] has gone into combining the strengths of different methods into more efficient and performant models via a two stage approach.”
- ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis (2021, background): “Most works [55, 18, 53] learn a discrete representation which is subsequently modeled autoregressively but approaches using continuous representations in combination with VAEs [10], or normalizing flows [1, 57, 17], exist too.”
- Taming Transformers for High-Resolution Image Synthesis (2020, methodology): “[18, 78] demonstrate similar gains when using an unconditional normalizing flow for the second stage, and [63, 64] when using a conditional normalizing flow.”
- CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP (2022, neutral): “, StyleGAN [17, 37]) and attempt manipulate the style space based on textual inputs [1, 4, 14, 24, 31, 38], or rely on a pre-trained textimage matching model (e.”
- Towards a Universal Continuous Knowledge Base (2020, background): “Rombach and Esser (2020) propose a conditional invertible neural network to translate between fixed representations from different off-the-shelf models.”
- Adversarial Text-to-Image Synthesis: A Review (2021, methodology): “In [88], the authors train an invertible network [89, 90] to fuse the pre-trained, expert networks BERT [39] and BigGAN [84], translate between their representations and reuse them for text-to-image synthesis.”
- Stochastic Image-to-Video Synthesis using cINNs (2021, methodology): “[67] Robin Rombach, Patrick Esser, and Björn Ommer.”