Generative models improve image probability estimation but lack interpretability.
problem Lack of interpretability in generative models for natural image distributions.
method Extracted explicit probability density estimates from GANs and analyzed latent representations.
result Natural image density functions are difficult to interpret.
A new method for comparing image probability measures using convolution operators.
problem Efficiently comparing images using conventional sliced Wasserstein methods.
method Proposed convolution sliced Wasserstein (CSW) methods with stride, dilation, and non-linear activation.
result CSW demonstrates favorable performance over conventional sliced Wasserstein in image comparison and deep generative modeling.
Bayesian imaging methods deliver trustworthy probabilities in some cases but struggle with uncertainty quantification.
problem Uncertainty quantification in Bayesian imaging methods.
method Monte Carlo method to explore reliability of probabilities.
result Modern Bayesian imaging techniques deliver reliable probabilities in some cases but not for uncertainty quantification.
An image pattern can be represented by a probability distribution whose density is concentrated on different low-dimensional subspaces in the high-dimensional image space. Such probability densities have an astronomical number of local modes corresponding to typical pattern appearances. Related groups of modes can join…
A method to monitor probability predictions for calibration loss in image classification models.
problem Maintaining calibration in machine learning predictions over time.
method Cumulative sum-based approach with dynamic limits for early detection of miscalibration.
result Early detection of operational context changes impacting image classification performance.
Paper explores CapsNet for anomaly detection in images.
problem Detecting anomalies in unseen images using CapsNet.
method Developed prediction-probability and reconstruction-error based normality scores.
result CapsNet-based methods outperform benchmarks in anomaly detection.
Bayesian tensor network reduces conditional probability calculation to polynomial time.
problem Exponential cost of calculating conditional probabilities for multiple events.
method Bayesian tensor network (BTN) with polynomial complexity.
result Competitive performance in image recognition with simple tree structures.
A method for diffusion on probability simplex for generative models.
problem Tension between continuous and discrete data in diffusion models.
method Proposes using softmax function applied to Ornstein-Uhlenbeck Process on probability simplex.
result Method extends to bounded image generation.
The paper improves the probability flow ODE sampler for faster sampling of natural images.
problem Improving the convergence rate of the probability flow ODE sampler.
method Adapting the probability flow ODE sampler to exploit intrinsic low-dimensional structures in natural image data.
result Achieves a dimension-free convergence rate of O(k/T) in total variation distance, improving upon existing results. New Fourier metrics equivalent to Wasserstein distances in image processing.
problem Equivalence of Fourier-based and Wasserstein metrics in imaging problems.
method Extensions of Fourier-based metrics to handle different centers of mass and discrete measures, showing equivalence to Wasserstein distances.
result New Fourier metrics are equivalent to Wasserstein distances with explicit constants, improving runtime in image processing.
Wassmap reduces image complexity while preserving key features.
problem Global nonlinear dimensionality reduction in imaging.
method Wassmap uses Wasserstein space and pairwise distances to create isometric embeddings.
result Wassmap can recover parameters of image manifolds like translations and dilations.
New method for generating images with conditional probability models.
problem Generating images with specific conditions.
method Score-based diffusion models with theoretical analysis and new estimator.
result New estimator for conditional score performs similarly to state-of-the-art.
Paper predicts embryo implantation probability from IVF time-lapse imaging.
problem Manual embryo selection in IVF has low success rates.
method Data-driven system trained on time-lapse imaging videos.
result Algorithm improves positive and negative predictive values.
Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propose a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via the use of the Poisson and binomial probability distributions. We evaluate …
Energy-based models can generate complex images by combining simpler concepts.
problem Generating natural images that satisfy complex logical combinations of concepts.
method Energy-based models combine probability distributions of simpler concepts to generate compositions.
result Energy-based models can generate images that satisfy conjunctions, disjunctions, and negations of concepts.
New method calibrates classifier probabilities with guaranteed coverage.
problem Inaccurate probability estimates by classifiers in high-risk applications.
method Adaptive temperature scaling algorithm for conformal prediction.
result Improves calibration error measures and standard metrics across various tasks.
Improves reliability of medical diagnosis uncertainty estimates.
problem Label uncertainty in medical diagnosis.
method Post-hoc alpha-calibration method for neural network classifiers. result Significantly enhances reliability of uncertainty estimates.
Defines knotting probability for spatial arcs.
problem Calculating the probability of knotting in spatial arcs.
method Projection of spatial arcs to planes, defining knotting probability for diagrams.
result Knotting probability defined for every oriented spatial arc.
Paper uses RL to fool neural networks with minimal pixel changes.
problem Vulnerability of neural networks to adversarial attacks.
method Deep Q Learning (DQN) to generate adversarial images.
result DQN can fool neural networks with minimal pixel modifications.
TSSC images enhance chaotic signal classification using ConvNets.
problem Classifying chaotic signals accurately and robustly.
method Triad State Space Construction (TSSC) for image encoding, Convolutional Neural Network (ConvNet) for classification.
result TSSC-ConvNet achieves high accuracy and robustness in chaotic signal classification.
Convolutional neural networks improve image classification accuracy.
problem Improving accuracy in image classification.
method Analyzing the convergence rate of misclassification risk for image classifiers.
result A rate of convergence independent of image dimension proves the effectiveness of CNNs.
This work improves deep neural network probability estimation methods.
problem Estimating probabilities from high-dimensional data with inherent uncertainty.
method Investigates and compares methods for probability estimation using deep neural networks, proposing a new method that promotes consistent probabilities.
result The new method outperforms existing approaches on most metrics on simulated and real-world data.
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstructi…
DDPM encoder matches optimal transport for natural images.
problem Understanding theoretical properties of DDPM latent space.
method Showed DDPM encoder matches optimal transport for common distributions.
result DDPM encoder map coincides with optimal transport map for natural images.
DistillKac generates images quickly using damped wave equations.
problem Generating high-quality images efficiently.
method Uses damped wave equations and Kac dynamics for finite speed transport.
result Fast image generation with high quality and numerical stability.
StoryGen uses images to generate coherent text from multiple images.
problem Generating coherent text from multiple images.
method Designing a Relational Text Data Generator algorithm to relate features from plural images.
result The model can generate meaningful paragraphs containing extracted features from input images.
New method optimizes MRI sampling patterns for faster scans.
problem Accelerate MRI scans without sacrificing image quality.
method Joint learning of adaptive sampling patterns and model-based recovery.
result Improved MR image quality compared to other methods.
Dasgupta and Shulman showed that a two-round variant of the EM algorithm can learn mixture of Gaussian distributions with near optimal precision with high probability if the Gaussian distributions are well separated and if the dimension is sufficiently high. In this paper, we generalize their theory to learning mixture…
Study bounds deepfake detection error probability using robust statistics.
problem Limiting error in deepfake detection.
method Formulated as hypothesis testing, uses robust statistics and Euclidean approximation.
result Established relationships between error probability and network thresholds.
New method calibrates uncertainty estimates for image classifiers without labeled data.
problem Uncertainty estimates for modern classifiers are unreliable without labeled calibration data.
method Calibrates uncertainty estimates using unlabeled examples for distribution shifts.
result Proposes a method that provides excellent uncertainty estimates under natural distribution shifts.
End-to-end meta-learned system for image compression.
problem Reducing the gap between training and inference conditions in image compression.
method Model-Agnostic Meta-learning approach for latent tensor overfitting and updating encoder and decoder networks.
result Meta-learned system achieves better compression performance compared to traditional methods.
Proposes a new model for joint probability distributions in computer vision.
problem Limitation of existing models in meeting diverse downstream tasks.
method Uses parametric conditional probability distributions for each group of variables conditioned on the rest.
result Models can be used for any downstream task without task-specific design.
New method uses deep learning to solve linear inverse problems.
problem Solving linear inverse problems with high-dimensional signals.
method Stochastic coarse-to-fine gradient ascent procedure using implicit prior from denoising CNN.
result General algorithm for solving linear inverse problems without additional training.
Study formalizes properties of deep neural networks for image recognition.
problem Understanding why deep neural networks classify images.
method Formalized properties of eight state-of-the-art deep neural networks.
result Deep neural networks can be deceived with a fooling ratio of 88%.
Develops statistical guarantees for image-to-image regression models.
problem Current image-to-image regression models lack statistical guarantees for model mistakes and hallucinations.
method Uncertainty quantification techniques with rigorous statistical guarantees for image-to-image regression problems.
result Derives uncertainty intervals around each pixel with formal mathematical guarantees.
New method provides formal uncertainty guarantees for image classifiers.
problem Uncertainty quantification for image classifiers without formal guarantees.
method Adapts conformal prediction to give stable, formal coverage guarantees.
result Method outperforms existing approaches in coverage and set size.
mFI-PSO generates effective adversarial images for DNNs.
problem Vulnerability of DNNs to small perturbations in images.
method Uses mFI for pixel selection and PSO for objective functions.
result mFI-PSO effectively designs flexible adversarial images.
EBM life cycle project improves MCMC for image generation, defense, and density modeling.
problem Improving MCMC for diverse EBM applications.
method Three novel MCMC initialization methods for negative samples.
result State-of-the-art performance gains across three applications.
Framework synthesizes geological images minimizing patch distribution discrepancy.
problem Synthesizing realistic geological images from a single exemplar.
method Uses kernel discrepancies and generative neural networks for efficient synthesis.
result Synthesized images match visual patterns and spatial statistics of the exemplar.
RealStats detects fake images rigorously, combining multiple detectors for robustness.
problem Detecting AI-generated images remains challenging due to evolving generative models.
method Combines training-free statistics to compute p-values and aggregate them for a unified real-image distribution.
result Framework produces interpretable probability scores for robust fake image detection.
A 3-stage method enhances hyperspectral image classification accuracy.
problem Classifying detailed classes in hyperspectral images with limited labeled data.
method Uses Nested Sliding Window and PCA for spatial consistency, SVM for spectral estimation, and TV model for spatial smoothing.
result Our method outperforms state-of-the-art algorithms, especially in scenarios with small training sets.
Paper reduces Hausdorff Distance in medical image segmentation.
problem Reduction of Hausdorff Distance in medical image segmentation.
method Three novel loss functions for training CNNs to estimate and reduce HD.
result Approximately 18-45% reduction in HD without degrading other performance metrics.
CoRAS adapts image acquisition rates for accurate reconstruction.
problem Determining when enough measurements are collected for accurate image reconstruction.
method Adaptive acquisition rate selection based on reconstruction error probability.
result CoRAS achieves target stopping-time coverage with fewer measurements.
CSI method learns conditional distributions by estimating flow equations.
problem Learning conditional distributions in generative models.
method Estimates probability flow equations to transport reference to target distribution.
result Derives explicit expressions for conditional drift and score functions.
The paper extends optimal transport for linear separability of sheared distributions in supervised learning.
problem Learning on the space of probability measures using shifts and scalings.
method Embedding probability measures into L2 spaces using optimal transport, then applying regular machine learning techniques. result Sheared distributions can be linearly separated under certain conditions, with bounds on transformations.
Proposes a framework for quantifying aleatoric uncertainty in image restoration.
problem Quantifying aleatoric uncertainty in image restoration problems.
method Divides conditional probability modeling into deterministic and stochastic levels, enabling efficient sampling and regularization.
result Shows significant potential in giving state-of-the-art point estimates and associated uncertainty information.
Proposes a new model to generate informative samples for image classification without querying an oracle.
problem Expensive and difficult labeling of data for machine learning models.
method Integrates active learning with a conditional GAN to generate samples with high uncertainty for a specific label.
result Generated samples improve image classification performance without querying an oracle.
Paper proposes a new framework for hypothesis testing in imaging.
problem Challenges in hypothesis testing for imaging data.
method Combines self-supervised imaging, vision-language models, and non-parametric hypothesis testing.
result Demonstrates improved power and robust error control in image-based phenotyping.