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Found 4 papers, 1 papers with code
Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
Whilst diffusion probabilistic models can generate high quality image content, key limitations remain in terms of both generating high-resolution imagery and their associated high computational requirements.
Ranked #4 on
Image Generation
on LSUN Bedroom 256 x 256
(Recall metric)
UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models
Our method, UNpaired Image Translation with Denoising Diffusion Probabilistic Models (UNIT-DDPM), trains a generative model to infer the joint distribution of images over both domains as a Markov chain by minimising a denoising score matching objective conditioned on the other domain.
Model-based approach for analyzing prevalence of nuclear cataracts in elderly residents
However, the prevalence of nuclear cataracts is higher in elderly people in tropical areas than in temperate areas.
Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery
However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep learning driven detection and classification approaches.
