Paper explores VRM for PSMLC with partially labeled medical images.
problem Improving PSMLC with limited labeled data.
method Applies VRM to PSMLC for better model performance.
result VRM improves PSMLC performance with partial labels.
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48 results for “Partial Image Generation”
PFDL improves deep learning models' OOD generalization by decorrelating feature embeddings.
problem Out-of-distribution generalization in deep learning models.
method PFDL algorithm that optimizes feature decomposition network and image classification model.
result PFDL improves the accuracy of image classification models on OOD datasets.
This research uses Siamese networks to identify partial mouse brain images from the Allen atlas.
problem Identifying precise mouse brain microscopy images from the Allen atlas.
method Siamese Networks with contrastive learning to find corresponding atlas plates for partial images.
result Siamese CNNs achieved 25% TOP-1 and 100% TOP-5 accuracy in identifying brain slices from the Allen atlas.
Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In the…
Paper proposes efficient method for evaluating Bayesian models in imaging.
problem Evaluation of Bayesian models in imaging when ground truth is unavailable.
method Novel combination of Bayesian cross-validation and data fission for unsupervised model selection and misspecification detection.
result Achieved excellent selection and detection accuracy with low computational cost.
Traditional classifiers can generate high-quality images comparable to generative models.
problem Separation between classifiers and generators in neural networks.
method Optimizing input gradients to produce images, using mask-based stochastic reconstruction, progressive-resolution technique, and distance metric loss.
result Traditional classifiers can generate high-fidelity images of 256256 resolution on ImageNet.
EnSF uses image inpainting to handle partial observations in data assimilation.
problem Data assimilation challenges with partial observations.
method EnSF integrates image inpainting with diffusion models to predict unobserved states.
result EnSF successfully tracks SQG dynamics with partial observations.
Paper proposes a weak supervision technique for CNN semantic segmentation of lung diseases using partially annotated data.
problem Creating annotated datasets for semantic segmentation of lung diseases is laborious and time-consuming.
method Proposes a weak supervision technique that utilizes partially annotated datasets to improve CNN semantic segmentation accuracy.
result Significantly improved segmentation accuracy using partially annotated datasets.
Neural DEs improve single image super-resolution.
problem Challenging tasks in image super-resolution.
method Applied Neural Differential Equations to image super-resolution, using variational methods and backpropagation.
result Differential models match state-of-the-art performance.
Let be a holomorphic fibration with compact fibers and a relatively ample line bundle over . We obtain the asymptotic of the curvature of -metric and Qullien metric on the direct image bundle up to the lower order terms than for la…
A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learningcs.LG
A robot learns to classify images with limited perception using a layered reinforcement learning approach.
problem Image classification for robots with partial perception.
method Three-layer architecture using deep reinforcement learning, including meta-layer, action-layer, and classification-layer.
result The method achieves high accuracy on the MNIST dataset and provides explainability of the agent's decision-making process.
New partial models correct for confounding effects in reinforcement learning.
problem Confounding effects in partial models lead to incorrect planning.
method Introduces causally correct partial models for reinforcement learning.
result Causally correct partial models avoid confounding effects and improve planning accuracy.
Study controllability of diffeomorphisms of simple polytopes.
problem Controllability of diffeomorphisms of simple polytopes.
method Lie group structure and controllability results for diffeomorphisms of simple polytopes.
result Identity component of the diffeomorphism group is generated by the exponential image.
Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.…
Mobile agents classify images via reinforcement learning and consensus.
problem Image classification using multiple mobile agents.
method Proposed network architecture for local belief formation and feature extraction. Decentralized consensus protocol using reinforcement learning.
result Effectiveness of the proposed framework demonstrated on MNIST dataset.
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation …
Compressed sensing is a powerful tool in applications such as magnetic resonance imaging (MRI). It enables accurate recovery of images from highly undersampled measurements by exploiting the sparsity of the images or image patches in a transform domain or dictionary. In this work, we focus on blind compressed sensing (…
Generative models solve medical imaging inverse problems without needing paired data.
problem Reconstructing medical images from partial measurements.
method Score-based generative models trained on medical images, then sampling to reconstruct images consistent with measurements and physical model.
result Comparable or better performance in CT and MRI tasks, with improved generalization to unknown measurement processes.
We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high dimensional space. Unlike …
Proposes a new GAN architecture for generating data conditioned on partial information.
problem Generating data conditioned on partial ancillary information.
method Introduces a new Adversarial Network architecture and training strategy.
result The proposed method outperforms standard Conditional GANs in generating data under partial conditioning.
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priorscs.LG
Paper offers robust recovery for 1-bit sensing with partial Gaussian circulant matrices.
problem Accurately recovering vectors from 1-bit measurements using structured matrices.
method Correlation-based optimization with randomly signed partial Gaussian circulant matrices and generative models.
result Recovery guarantees match those for i.i.d. Gaussian matrices but with faster computation.
Defense against small image patches using occlusions.
problem Vulnerability of deep learning to small adversarial patches.
method Partially occlude image around each patch location.
result Certified security against patch attacks of a certain size.
Improved person detection in occluded conditions with AOS images.
problem Inaccurate classification of partially occluded people, animals, or objects.
method Combining multi-perspective images captured by Airborne Optical Sectioning (AOS) to achieve better precision and recall.
result Precision/recall of 96/93% for automated person detection.
Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT. This property has been heavily exploited in various applications in image processing and medical imaging. Compressed sensing exploits the sparsity of images or image patches in a transform domain or synth…
BNCR-GAN improves GANs to generate clean images from degraded inputs.
problem Generating clean images from blurred, noisy, and compressed degraded inputs.
method Multiple-generator model with image, blur-kernel, noise, and quality-factor generators, using masking architectures and adaptive consistency losses.
result BNCR-GAN effectively learns clean image generators from degraded images without degradation parameters.
New method denoises and fills in missing image data without clean training data.
problem Denoising and inpainting images with noisy, incomplete data.
method Robust Hadamard Autoencoders trained on noisy, incomplete data.
result Autoencoders can perform denoising and inpainting simultaneously.
Paper proposes a classifier to improve medical image classification with limited data.
problem Limited training data leads to overfitting in medical image classification.
method Uses reinforcement learning to update classifier parameters with generalization feedback from a subset of training data.
result Improves classification performance and demonstrates generalized learning.
New method separates objects from images using deep neural networks trained to inpaint.
problem Fully self-supervised instance separation of occluded objects in images.
method Maximizes independence of two image regions given a fully self-supervised inpainting network.
result Method achieves similar segmentation performance to fully supervised methods on microscopy image datasets.
Expands weak supervision by allowing partial labels from multiple noisy sources.
problem Creating models without labeled data using heuristic labelers.
method Probabilistic generative model estimating partial label accuracies.
result Improved model accuracy on various tasks (8.6% on text, comparable to zero-shot methods on images).
Geometric models improve feature extraction and equivariance in image generation.
problem Improving feature extraction at multiscale levels and reducing network complexity.
method Proposes a geometric generative model based on morphological PDEs and GANs, incorporating equivariance for geometric interpretability.
result Preliminary results show GM-GAN outperforms classical GANs on MNIST data.
Partial differential equations (PDEs) are indispensable for modeling many physical phenomena and also commonly used for solving image processing tasks. In the latter area, PDE-based approaches interpret image data as discretizations of multivariate functions and the output of image processing algorithms as solutions to…
DSRGAN learns independent structure and rendering without tuple supervision.
problem Learning disentangled representation for natural image generation without tuple supervision.
method Introducing an auxiliary domain with a common underlying-structure space, and designing a parallel generative network with a common Progressive Rendering Architecture.
result DSRGAN significantly outperforms state-of-the-art methods in disentanglability.
COCO-GAN generates images by parts using spatial coordinates, achieving state-of-the-art quality.
problem Generating full images from partial observations due to computational constraints.
method COCO-GAN uses conditional coordination to generate images by parts based on spatial coordinates, with a generator and discriminator learning to justify realism.
result COCO-GAN produces state-of-the-art-quality full images during inference, enabling novel applications like beyond-boundary generation and panorama generation.
Boomerang generates nonidentical images similar to input on image manifolds.
problem Generating nonidentical images similar to input on image manifolds.
method Adding noise to input image, moving closer to latent space, and mapping back through partial reverse diffusion.
result Boomerang generates nonidentical images similar to input on image manifolds.
New method trains reflected Schrödinger bridges without complex derivatives.
problem Training reflected Schrödinger bridges efficiently in high dimensions.
method Partially simulation-free framework with new sampling method.
result Generative performance maintained or slightly improved with reflected dynamics.
Proposes neural network for causal inference with multimodal data.
problem Estimating causal effects with text and image data as confounders.
method Double machine learning framework adapted to partially linear models, semi-synthetic dataset generation.
result Improved performance in causal effect estimation with multimodal data.
In Ahlfors' covering surface theory, it is well known that there exists a positive constant such that for any nonconstant holomorphic mapping if then% A(f,Δ)\leq hL(f,\partial Δ),% where is the disk in is the unit Riemann sphere,…
R-PLS improves analysis of brain functional connectivity matrices.
problem Improving analysis of functional connectivity matrices in brain imaging.
method Introducing R-PLS, a generalization of PLS for symmetric positive definite matrices.
result R-PLS identifies key functional connections in brain imaging datasets.
Research on unique continuation principles in medical and seismic imaging.
problem Understanding unique continuation principles for inverse problems.
method Integral geometry and fractional calculus methods applied to various imaging problems.
result Developed new techniques for solving inverse problems with partial data.
A method for self-supervised representation learning in partially observable environments.
problem Sparse rewards and stochasticity in partially observable environments.
method World model in latent space for estimating missing information.
result Significant improvement in exploration compared to prior work.
Framework learns image dynamics between time steps using latent variables.
problem Challenges in capturing evolving image patterns and temporal information.
method Estimates intermediary image stages using a physical latent variable model.
result Demonstrates robustness and effectiveness in geoscientific imagery.
A new model improves medical image segmentation uncertainty.
problem Uncertainty in medical image segmentation.
method Conditional Normalizing Flow (cFlow) for improved segmentation uncertainty.
result Improved quality and diversity of segmentation samples.
Paper proves direct image sheaf positivity for certain Kähler fibrations.
problem Proving positivity of direct image sheaves for Kähler fibrations.
method Uses singular Hermitian line bundles and Narasimhan-Simha metric.
result Direct image sheaf is singular Nakano semi-positive.
The asymptotics of the -curvature and the second variation of analytic torsion on Teichmüller spacemath.DG
We consider the relative canonical line bundle and a relatively ample line bundle over the total space of fibration over the Teichmüller space by Riemann surfaces. We consider the case when the induced metric $\sqrt{-1}\partial\bar{\partial}φ|_{\…
We show that the notion of -hyperconvexity on oriented flag manifolds defines a partial cyclic order. Using the notion of interval given by this partial cyclic order, we construct Schottky groups and show that they correspond to images of positive representations in the sense of Fock and Goncharov. We construct poly…
A novel capsule network model improves surrogate modeling and uncertainty quantification from sparse data.
problem Surrogate modeling and uncertainty quantification of systems from sparse data.
method Adapted Capsule Network (CapsNet) architecture into image-to-image regression encoder-decoder network.
result The proposed approach accurately, efficiently, and robustly predicts responses for arbitrary diffusion fields.
Study character varieties of tangles to map immersed curves in the pillowcase.
problem Characterizing holonomy-perturbed traceless SU(2) character varieties.
method Examining marked tangles as endomorphisms in the cobordism category and using holonomy-perturbed traceless character variety functor.
result Endomorphisms of immersed curves in the pillowcase have the same image.
The paper explores how neural networks generalize differently from natural and medical images.
problem Discrepancies in generalization error between natural and medical images.
method Established and empirically validated a generalization scaling law with respect to intrinsic dataset properties.
result Higher intrinsic 'label sharpness' of medical images leads to higher adversarial vulnerability.