Paper proposes a method to enhance low-quality retinal images using optimal transport.
problem Artifacts and imperfections in retinal images lead to diagnostic inaccuracies.
method Leveraging optimal transport theory, an unpaired image-to-image translation scheme is proposed.
result The method improves perceptually and quantitatively the quality of low-quality retinal images.
Deep morphing detects bone structures in low-quality X-ray images.
problem Detecting bone structures in low-quality fluoroscopic X-ray images.
method Two-stage deep learning approach using deep segmentation networks and statistical shape models.
result Efficiently detects bone structures in low-quality X-ray images.
Dataset of 50k hotels aids in human trafficking investigations.
problem Identify hotels from low-quality, occluded images.
method Curated dataset of 1M annotated hotel room images, baseline model with data augmentation.
result Demonstrated the feasibility of hotel recognition from challenging images.
Proposes a cross-scale residual network for multiple image restoration tasks.
problem Image restoration tasks (super-resolution, denoising, deblocking) have strong correlations.
method Cross-scale residual network exploiting scale-related features and inter-task correlations.
result Outperforms state-of-the-art methods in multiple image restoration tasks.
New SSIM measure improves adversarial attacks' perceptual quality.
problem Lack of perceptual quality in traditional adversarial attacks.
method Replaced Lp norms with SSIM measure for better quality. result SSIM-constrained attacks achieve better quality and similar success rate.
OTRE uses OT to improve retinal images, outperforming existing methods.
problem Improving quality of non-mydriatic retinal images for accurate diagnoses.
method OT theory for image-to-image translation, regularization by enhancing.
result OTRE outperforms state-of-the-art methods on various retinal image tasks.
Thanks to the growing availability of spoofing databases and rapid advances in using them, systems for detecting voice spoofing attacks are becoming more and more capable, and error rates close to zero are being reached for the ASVspoof2015 database. However, speech synthesis and voice conversion paradigms that are not…
DVGS identifies low-quality data quickly and accurately.
problem Identifying and filtering mislabeled or noisy data in machine learning.
method Data Valuation with Gradient Similarity (DVGS) algorithm.
result DVGS outperforms baseline methods in identifying low-value data across various domains.
Improves text-to-image diffusion models using GFlowNets.
problem Aligning diffusion models with text descriptions.
method Post-training diffusion models with GFlowNets to generate high-reward images.
result Effective alignment of large-scale text-to-image diffusion models with reward information.
Improves image quality in generative models by estimating pixel-wise aleatoric uncertainty.
problem Lack of quantitative assessment of image quality in diffusion models.
method Estimate pixel-wise aleatoric uncertainty during sampling phase using a perturbation scheme designed for diffusion models.
result Uncertainty-guided sampling leads to better sample generation quality as shown by FID scores.
Paper introduces DACAL for high-resolution photo and video enhancement.
problem Photo and video enhancement with weak supervision.
method Divide-and-conquer adversarial learning approach with hierarchical decomposition.
result State-of-the-art performance in high-resolution photo and video enhancement.
CNN improves frame selection for ultrasound elastography.
problem Choosing suitable frames for accurate strain estimation in ultrasound elastography.
method Convolutional Neural Network (CNN) for frame selection.
result CNN selects frames in 5.4 ms for high-quality strain images.
Dynamic defense against Byzantine poisoning in federated learning.
problem Byzantine poisoning attacks on federated learning models.
method Dynamic federated aggregation operator to filter out adversarial clients.
result Dynamic selection of clients improves global learning model performance.
Edge device deep learning improved with noise handling model.
problem Noise and low quality data degrade deep learning performance on edge devices.
method Mixture of Pre-processing Experts (MoPE) model with adversarially trained autoencoder.
result The MoPE model achieves better accuracy on noisy images without sacrificing clean image accuracy.
MRI image quality affects statistical and predictive analysis of brain morphology.
problem Impact of MRI image quality on statistical and predictive analysis of brain morphology.
method Systematic testing of image quality on univariate statistics and machine learning classification using three large datasets.
result Low-quality MRI data significantly affects detecting significant sex/gender differences in smaller samples, but not in larger ones.
Improved deep RL by pre-training on human demonstrations.
problem Data inefficiency in deep RL learning from raw images.
method Pre-training on non-expert human demonstrations using supervised learning.
result Significant improvements in learning speed with noisy and low-quality demonstrations.
Generative models predict page quality without training, useful for low-resource settings.
problem Detecting low-quality content in web articles.
method Human evaluation and analysis of 500 million web articles.
result Generative models can predict page quality without training, useful for low-resource settings.
New deep learning method improves 4D Flow MRI super-resolution under domain shift.
problem Domain shift in low-resolution 4D Flow MRI data.
method Distributional deep learning framework for domain generalization.
result Framework significantly outperforms traditional methods in real data applications.
ECG-DelNet uses neural networks to accurately delineate ECGs, even with low-quality data.
problem Lack of model explainability and small databases limit deep learning applicability in ECG detection.
method Adapted U-Net architecture for 1D data, used PhysioNet's QT database, applied data augmentation and regularization techniques.
result Best configuration achieved high precision and recall for P, QRS, and T waves.
Study shows online learning algorithms incentivize low-quality content, proposing new algorithms to improve quality.
problem Online learning algorithms in content recommender systems incentivize producers to create low-quality content.
method Analyzed the game between producers and content quality, designed new learning algorithms to incentivize high effort and quality.
result New algorithms incentivize producers to invest high effort and achieve high user welfare, improving content quality.
CALLISTO generates tests and assesses ML data quality using prediction entropy.
problem Validating ML systems for accuracy and data quality.
method Entropy-based test generation and data quality assessment framework.
result CALLISTO detects up to 20x more errors than traditional methods.
GeNAS uses genetic algorithms to automatically assess human sperm morphology.
problem Manual sperm morphology analysis is inexact, subjective, and hard to teach.
method Genetic Neural Architecture Search (GeNAS) for automatic SMA.
result GeNAS achieved state-of-the-art results in sperm morphology detection.
RDPD uses KD to improve models trained on poor data using rich data from other environments.
problem Deploying models in environments with varying data quality and quantity.
method Knowledge distillation from high-quality, rich data to improve low-quality, poor data models.
result RDPD models consistently outperform baselines and state-of-the-art methods across multiple datasets.
Simplified MDNs quantify uncertainty in large complex datasets.
problem Uncertainty quantification in large scale complex data.
method Simplified Mixture Density Networks (MDNs) for regression problems.
result Improved predictive log-likelihood and root-mean-square-error compared to existing methods.
LARP filters data to protect model performance across various learners.
problem Protecting model accuracy in public datasets with diverse learners.
method Formalizes and analyzes LARP, a robust data prefiltering method.
result LARP provides guarantees on worst-case loss over a set of learners, with some performance trade-off.
Estimates future ICU demand using age-structured data and logistic modeling.
problem Projecting future ICU demand from low-quality data.
method Age-structured sampling, logistic fit, subnotification factor correction.
result Corrected logistic curve projects future ICU demand.
DDMI generates high-quality INRs by adapting positional embeddings.
problem Existing INR generative models fail to produce high-quality representations.
method DDMI uses adaptive positional embeddings and a D2C-VAE to enhance expressive power.
result DDMI outperforms existing models across multiple modalities and datasets.
Study on sparse recovery with mixed-quality data, establishing sample-size conditions.
problem Sparse recovery with heterogeneous noise from high- and low-quality sources.
method Establishes linear trade-off for sufficient conditions, analyzes LASSO algorithm.
result Linear trade-off for sufficient conditions, robustness of LASSO to data heterogeneity.
This paper presents a dynamic model to study the impact on the economic outcomes in different societies during the Malthusian Era of individualism (time spent working alone) and collectivism (complementary time spent working with others). The model is driven by opposing forces: a greater degree of collectivism provides…
New metric evaluates generative models across domains, diagnosing fidelity, diversity, and generalization.
problem Evaluating generative models in diverse domains with limited metrics.
method Introduces a 3D evaluation metric (α-Precision, β-Recall, Authenticity) for domain-agnostic diagnostics. result Unified metric characterizes fidelity, diversity, and generalization, diagnosing model performance.
Accelerates data generation in score-based models.
problem Slow generation of realistic data by score-based models.
method Developed an adaptive step size SDE solver.
result Generates data 2-10 times faster with high quality.
Credit scores misclassify borrowers, especially minorities, leading to inequitable access.
problem Misclassification of borrowers by credit scores, particularly minorities.
method Benchmarked a widely used credit score against a machine learning model.
result Machine learning model improves predictive accuracy for low-quality data, leading to more equitable access.
No GANs can learn disconnected manifolds precisely.
problem Learning disconnected manifolds is challenging due to unimodal latent distributions.
method Formalized a no free lunch theorem and derived a rejection sampling method.
result Upper bound on the precision of the targeted disconnected manifold distribution.
We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query bas…
Proposes a new loss function for robust training of deep neural networks against noisy labels.
problem Training deep neural networks with noisy labels, especially instance-independent noise.
method Introduces a novel information-theoretic loss function, L_DMI, based on Determinant based Mutual Information (DMI).
result L_DMI is the first provably robust loss function to instance-independent label noise, without requiring auxiliary information.
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability. The recently proposed Wasserstein GAN (WGAN) makes progress toward stable training of GANs, but sometimes can still generate only low-quality samples or fail to converge. We find that these problems are often du…
Improves disentangled representation learning with multi-stage modeling.
problem Trade-off between disentanglement and reconstruction quality in autoencoders.
method Penalty-based disentanglement learning followed by detail information modeling.
result Higher reconstruction quality than state-of-the-art methods with equivalent disentanglement.
The quality of an induced model by a learning algorithm is dependent on the quality of the training data and the hyper-parameters supplied to the learning algorithm. Prior work has shown that improving the quality of the training data (i.e., by removing low quality instances) or tuning the learning algorithm hyper-para…
Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistica…
C-VAE improves VAE by resolving prior issues and generating better samples.
problem Low-quality samples from VAE due to prior issues.
method Formulates VAE as OT, allows flexible priors, and uses OT formulations.
result C-VAE generates higher quality samples and latent representations.
A novel incentive mechanism improves fairness and participation in federated learning.
problem Low-quality clients and lack of fairness in federated learning.
method Client selection process and money transfer mechanism to ensure fairness and participation.
result The proposed incentive mechanism improves the duration and fairness of federated learning.
In this paper, we consider a new low-quality label learning problem: learning time series detection models from temporally imprecise labels. In this problem, the data consist of a set of input time series, and supervision is provided by a sequence of noisy time stamps corresponding to the occurrence of positive class e…
This work introduces a noise-adaptive conformal inference method for better prediction sets in noisy data.
problem Real-world complications like random label noise limit the effectiveness of conformal inference.
method An adaptive conformal inference method capable of handling deviations from exchangeability.
result Informative prediction sets with tight marginal coverage guarantees in noisy data.
Paper proposes Universum GANs to improve GANs with limited labeled data.
problem Limited labeled data makes supervised learning challenging.
method Proposes Universum GANs with evolving discriminator loss.
result Improved discriminator accuracy and high quality data generation.
Enhances clustering by using external categorical data.
problem Improving clustering outcomes using external categorical evidence.
method Evidence transfer method that manipulates autoencoder latent representations based on external categorical data.
result Our method effectively manipulates latent representations with real evidence and remains robust with low quality evidence.
This note investigates the causes of the quality anomaly, which is one of the strongest and most scalable anomalies in equity markets. We explore two potential explanations. The "risk view", whereby investing in high quality firms is somehow riskier, so that the higher returns of a quality portfolio are a compensation …
Efficient synthetic data generation improves model performance on tabular data.
problem Improving model robustness and performance with scarce or low-quality data.
method Hardness characterization to identify high-value training points, generating synthetic data only from these points.
result Synthetic data generated from hardest points outperforms non-targeted methods on tabular datasets.
The paper proposes a method to detect and filter noisy or mislabeled data using pointwise mutual information.
problem Detecting and filtering noisy or mislabeled data in deep learning models.
method A mutual information-based framework quantifying statistical dependencies between inputs and labels.
result The method effectively filters low-quality samples, improving classification accuracy by up to 15%.