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Found 17 papers, 10 papers with code
Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers
Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment.
$\infty$-Diff: Infinite Resolution Diffusion with Subsampled Mollified States
This paper introduces $\infty$-Diff, a generative diffusion model defined in an infinite-dimensional Hilbert space, which can model infinite resolution data.
Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields
In this paper, we explore the use of an exact approach for calculating the IPE by using a pyramid-based integral formulation instead of an approximated conical-based one.
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise
A secondary problem is that Gaussian diffusion fails to capture larger anomalies; therefore we develop a multi-scale simplex noise diffusion process that gives control over the target anomaly size.
Ranked #21 on
Anomaly Detection
on VisA
Megapixel Image Generation with Step-Unrolled Denoising Autoencoders
An ongoing trend in generative modelling research has been to push sample resolutions higher whilst simultaneously reducing computational requirements for training and sampling.
MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray
Computed tomography (CT) is an effective medical imaging modality, widely used in the field of clinical medicine for the diagnosis of various pathologies.
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.
Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark
Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due to both negligent and malicious use.
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples.
Robust 3D U-Net Segmentation of Macular Holes
Macular holes are a common eye condition which result in visual impairment.
Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting
Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery.
Gradient Origin Networks
This paper proposes a new type of generative model that is able to quickly learn a latent representation without an encoder.
Segmentation of Macular Edema Datasets with Small Residual 3D U-Net Architectures
This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation.
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.
Learning protein conformational space by enforcing physics with convolutions and latent interpolations
Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function.
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text
In this work, we present a generic process flow for text recognition in scanned documents containing mixed handwritten and machine-printed text without the need to classify text in advance.
