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Attention-Guided Optimal Transport for Unsupervised Domain Adaptation with Class Structure Prior
Unsupervised domain adaptation(UDA) methods based on optimal transport have been successfully used to improve cross-domain classification...
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The impact of prior knowledge on causal structure learning
Causal Bayesian networks have become a powerful technology for reasoning under uncertainty in areas that require transparency and explainability, by...
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X2Vision: 3D CT Reconstruction from Biplanar X-Rays with Deep Structure Prior
We propose an unsupervised deep learning method to reconstruct a 3D tomographic image from biplanar X-rays, to reduce the number of required...
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Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays
The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a...
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Image decomposition combining low-rank and deep image prior
Most of the traditional variational decomposition models let the structure and texture belong to different functional spaces, which makes it...
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Constrained clustering with weak label prior
Clustering is widely exploited in data mining. It has been proved that embedding weak label prior into clustering is effective to promote its...
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Prior Knowledge-Based Intelligent Model for Lithology Classification
Vegetation coverage can weaken the lithology information and increases inter-class similarity, making it difficult to effectively extract key feature...
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Prior Work
The mutual exclusion problem has been investigated under a variety of modeling assumptions ever since the problem was first introduced by Dijkstra in...
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EpiRiskNet: incorporating graph structure and static data as prior knowledge for improved time-series forecasting
EpiRiskNet combines time-series data with graph and static information to enhance forecasting accuracy. This model features the SCI-Block for...
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Hybrid Prior-Based Diminished Reality for Indoor Panoramic Images
Due to the advancement of hardware technology, e.g. head-mounted display devices, augmented reality (AR) has been widely used. In AR, virtual objects...
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Towards adaptive graph neural networks via solving prior-data conflicts
Graph neural networks (GNNs) have achieved remarkable performance in a variety of graph-related tasks. Recent evidence in the GNN community shows...
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Material Microstructure Design Using VAE-Regression with a Multimodal Prior
We propose a variational autoencoder (VAE)-based model for building forward and inverse structure-property linkages, a problem of paramount...
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Edge-Prior Contrastive Transformer for Optic Cup and Optic Disc Segmentation
Optic Cup and Optic Disc segmentation plays a vital role in retinal image analysis, with significant implications for automated diagnosis. In fundus...
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Self-supervised Low-Light Image Enhancement via Histogram Equalization Prior
Deep learning-based methods for low-light image enhancement have achieved remarkable success. However, the requirement of enormous paired real data...
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Integrating prior knowledge to build transformer models
The big Artificial General Intelligence models inspire hot topics currently. The black box problems of Artificial Intelligence (AI) models still...
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Prior-SSL: A Thickness Distribution Prior and Uncertainty Guided Semi-supervised Learning Method for Choroidal Segmentation in OCT Images
Choroid structure is crucial for the diagnosis of ocular diseases, and deep supervised learning (SL) techniques have been widely applied to segment...
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Shape generation via learning an adaptive multimodal prior
Significant interest and progress have been drawn to the recent advancements in image creation using deep generative model, but the field of...
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Coding Prior-Driven JPEG Image Artifact Removal
Image priors play an important role in JPEG image artifact removal. However, most existing methods ignore the use of coding priors. This paper...
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Subspace Adaptation Prior for Few-Shot Learning
Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more...
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Structured prior distributions for the covariance matrix in latent factor models
Factor models are widely used for dimension reduction in the analysis of multivariate data. This is achieved through decomposition of a
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