71 citations · 237 across the 19 of their papers we have counts for
5 papers · 1 filter
Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes
Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki +2
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…
UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models
Hiroshi Sasaki, Chris G. Willcocks, Toby P. Breckon
We propose a novel unpaired image-to-image translation method that uses denoising diffusion probabilistic models without requiring adversarial training. Our method, UNpaired Image…
Robust 3D U-Net Segmentation of Macular Holes
Jonathan Frawley, Chris G. Willcocks, Maged Habib +3
Macular holes are a common eye condition which result in visual impairment. We look at the application of deep convolutional neural networks to the problem of macular hole segmenta…
Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark
Brian K. S. Isaac-Medina, Matt Poyser, Daniel Organisciak +3
Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use. For this reason, the automated detection and tracking of UAV is a…
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
Sam Bond-Taylor, Adam Leach, Yang Long +1
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected…