Study uses spoofing countermeasures to assess speech processing artifacts in voice conversion.
problem Difficulty in objectively assessing speech processing artifacts in voice conversion.
method Configured a constant-Q cepstral coefficient (CQC) model to measure artifact extent.
result Identified two clusters of VCC'18 entries: low-quality with detectable artifacts and higher quality with less artifacts.
Study compares EEG and fMRI systems, finding tradeoffs in artifact removal and classification accuracy.
problem Dealing with artifacts introduced by simultaneous EEG and fMRI recordings.
method Comparison of three MR compatible EEG recording systems, assessing their performance in single-trial EEG classification.
result Tradeoffs across systems, including setup ease and artifact removal methods.
Deep-learning improves 6x6-mm OCTA angiograms by reducing noise and artifacts.
problem Reduced scan quality in 6x6-mm OCTA angiograms due to undersampling.
method Deep-learning-based high-resolution angiogram reconstruction network (HARNet) trained on 3x3-mm and 6x6-mm angiogram data.
result Reconstructed 6x6-mm angiograms have lower noise and better vascular connectivity.
Study assesses CNN model robustness to noise in low-cost CT scans.
problem Evaluate CNN model performance on noisy, artifact-prone low-cost CT images.
method Developed and tested a CNN model for head CT triage, varying tube current and projections.
result Model remains robust to reduced tube current and fewer projections, maintaining AUROC close to original.
Integrated Assessment Models (IAMs) are mainstay tools for assessing the long-term interactions between climate and the economy and for deriving optimal policy responses in the form of carbon prices. IAMs have been criticized for controversial discount rate assumptions, arbitrary climate damage functions, and the inade…
Study benchmarks machine learning for removing EEG artifacts.
problem Removing artifacts from EEGs to improve clinical interpretation.
method Applied various machine learning algorithms to a large artifact recognition dataset.
result Established a benchmark for future research on artifact removal.
A new UNet variant reduces spectral artifacts in image transformations.
problem Spectral artifacts caused by traditional UNet upsampling layers.
method Introduced a Guided UNet (GUNet) architecture using a novel upsampling module.
result GUNet produces higher fidelity outputs in image transformations.
Improves visualization of high-dimensional data by correcting misleading artifacts in neighbor embedding methods.
problem Misleading visual artifacts in t-SNE and UMAP due to lack of data-independent manifold learning interpretations.
method LOO-map framework that extends embedding maps to the entire input space, identifying and correcting map discontinuities.
result Developed point-wise diagnostic scores to detect unreliable embedding points and improve hyperparameter selection.
New framework assesses AI hallucinations in inverse problems.
problem Artificial intelligence can produce incorrect details in imaging problems.
method Theoretical framework and algorithms to estimate and assess hallucinations.
result Developed necessary and sufficient conditions for hallucinations and computable bounds.
Single CNN removes multiple ultrasound artifacts.
problem Efficiently remove multiple ultrasound artifacts.
method OT-driven multi-domain unsupervised deep learning.
result Single neural network removes various artifacts.
Study examines how image artifacts impact polyp detection and proposes methods to mitigate their effects.
problem Impact of image artifacts on automated polyp detection accuracy.
method Systematic analysis of six artifact classes, investigation of learning without forgetting framework.
result Artifacts can either benefit or harm polyp detection; learning without forgetting can mitigate some harmful effects.
New method corrects motion artifacts in MR images without paired data.
problem Lack of paired data for supervised training in deep learning for MR motion correction.
method Outlier-rejecting bootstrap subsampling and aggregation, using optimal transport cycleGAN.
result Outperforms existing deep learning methods in correcting motion artifacts from TSM.
LLM-based trading systems vary in execution realism and reproducibility.
problem LLM-based trading systems vary in execution realism and reproducibility.
method Reproducibility audit of 30 trade-relevant primary studies.
result LLM-based trading systems vary in execution realism and reproducibility.
DORA analyzes deep neural networks' internal representations to detect spurious correlations.
problem Detecting spurious correlations in deep neural networks' internal representations.
method DORA uses Extreme-Activation (EA) distance measure to assess representation similarities.
result Identifies internal representations capable of detecting spurious correlations.
Automated MRI image quality assessment framework using machine learning.
problem Manual quality assessment of MRI images is time-consuming and costly.
method Machine learning model trained on human observer labels without reference images.
result Framework achieves 93.7% accuracy in estimating image quality.
Deep learning method reduces conebeam artifacts in CT imaging.
problem Conebeam artifacts in CT imaging due to cone angle.
method Differentiated backprojection domain deep learning for data-driven inversion.
result Our method outperforms existing iterative methods with reduced runtime complexity.
Proposes a fixed smooth convolutional layer to reduce checkerboard artifacts in CNNs.
problem Checkerboard artifacts in CNNs during upsampling and strided convolution.
method Fixed convolutional layer with adjustable smoothness, applied to four CNNs and GANs.
result Significantly improves classification performance and image generation quality.
Study geodesic X-ray transform and streaking artifacts on simple surfaces or spaces of constant curvature.
problem Streaking artifacts in CT images due to metal regions.
method Geodesic X-ray transform on nontrapping compact Riemannian manifolds with strictly convex boundaries.
result Streaking artifacts result from conormal singularities along common tangent geodesics.
Paper introduces adversarial lossy compression for video artifacts reduction.
problem Unpleasant reconstruction artifacts in standard video coding schemes at low bit-rates.
method Adversarial lossy video compression model minimizing an adversarial distortion objective.
result Reduction of perceptual artifacts and detail reconstruction under extreme compression.
Partial-input models fail to detect dataset artifacts, even when they perform poorly.
problem The effectiveness of partial-input models in detecting dataset artifacts is questionable.
method Design artificial datasets and identify trivial patterns in the SNLI dataset.
result Partial-input models can solve examples previously considered hard, indicating potential dataset artifacts.
Article examines stability of geodesic X-ray transforms and artifacts.
problem Stability of geodesic X-ray transforms and artifacts.
method Analyzes the impact of weight and geometry on stability, and examines artifacts in unstable cases.
result Landweber algorithm cannot provide accurate reconstruction in unstable cases.
Reduced-channel EEG systems struggle with artifact detection, highlighting the importance of referential channels.
problem Artifact detection in EEG signals with fewer channels.
method Investigated a deep learning algorithm, CNN-LSTM, on various channel configurations.
result False alarms increase dramatically when fewer channels are used, emphasizing the importance of referential channels.
Deep learning removes aliasing in MRI scans with fast computation.
problem High computational costs in MR scan reconstruction.
method Deep residual learning networks for magnitude and phase networks.
result Deep learning successfully removes aliasing artifacts from MRI scans.
A new unsupervised method removes CT metal artifacts using beta-CycleGAN and attention.
problem Metal artifact reduction in computed tomography (CT) images.
method Unsupervised learning using a beta-CycleGAN architecture with attention mechanism.
result Improved metal artifact removal that preserves image details.
Artifacts appear in broken ray transform due to conjugate points.
problem Artifacts in broken ray transform due to conjugate points.
method Integral transform over broken rays, analysis of conjugate points.
result Singularities cannot be recovered from local data, leading to artifacts.
Single model corrects JPEG artifacts for various compression settings.
problem JPEG compression artifacts due to aggressive quantization.
method Parameterized architecture using quantization matrix.
result State-of-the-art performance across different quality settings.
Automated malaria diagnosis from field slides achieves accurate results.
problem Challenges in analyzing field-prepared thin blood film microscopy images.
method Fully automated framework using machine learning, including CNNs trained on diverse field samples.
result Results are close to sufficient for drug resistance monitoring and clinical use-cases.
Paper explores unsupervised learning for ultrasound image artifact removal.
problem Improving visual quality of ultrasound images from various artifacts.
method Inspired by optimal transport cycleGAN, unsupervised deep learning for artifact removal.
result Unsupervised learning method provides comparable results to supervised learning.
Paper compresses neural network weight-updates for image artifacts removal.
problem Efficiently compressing neural network weight-updates for image artifacts removal.
method Fine-tuning a pre-trained artifact removal network on target data with a compression objective that encourages sparse and quantized weight-updates.
result Achieves reconstruction quality comparable to traditional codecs at comparable bitrates.
LLM generates coherent macroeconomic stress scenarios for portfolio risk assessment.
problem Macro-financial stress testing and portfolio risk assessment using traditional methods.
method Hybrid prompt-RAG pipeline combining structured prompting and retrieval of country fundamentals and news.
result LLM-generated scenarios yield stable tail-risk amplification with limited sensitivity to retrieval choices.
L-CNNs approximate gauge actions, revealing fixed points with no lattice artifacts.
problem Approximating gauge actions with lattice artifacts.
method Lattice gauge-equivariant convolutional neural networks (L-CNNs).
result L-CNNs provide fixed point actions with no lattice artifacts.
Generative model predicts menstrual cycle lengths accounting for self-tracking artifacts.
problem Uncertainty in self-tracked health data due to user adherence.
method Hierarchical, generative model using machine learning.
result Model yields state-of-the-art performance in predicting menstrual cycle lengths.
Detects non-causal artifacts in multivariate regression models.
problem Identifying non-causal associations in multivariate linear regression models.
method Uses ICA-based model to distinguish between causal and artifact associations by analyzing the orientation of regression coefficients relative to the covariance matrix.
result Regression vectors concentrate in low eigenvalue space for confounding and overfitting, distinguishing them from causal relationships.
A new geometric metric identifies true data changes from parametrization artifacts in high-dimensional representations.
problem Quantifying representation drift in high-dimensional data using Euclidean or cosine distances can misattribute changes due to arbitrary parametrizations.
method Introducing the Fubini Study metric to identify representations that differ only by gauge transformations.
result The Fubini Study metric isolates intrinsic evolution by remaining invariant under gauge-induced fluctuations, providing a diagnostic for meaningful structural changes.
Estimates support in distributions with sampling artifacts and errors.
problem Support estimation in the presence of sampling artifacts and errors.
method Regularized weighted Chebyshev approximations with Touchard polynomials, discretized semi-infinte programming.
result Significant improvements over noiseless support estimation methods.
NTK reveals order and chaos in DNNs, affecting checkerboard and border artifacts.
problem Checkerboard and border artifacts in DNNs.
method Analysis using Neural Tangent Kernel (NTK) in infinite-width setting.
result Transition between order and chaos regimes affects DNN performance.
Study assesses whether RL algorithm personalizes treatment sequences.
problem Evaluate if RL algorithm truly personalizes treatment sequences.
method Resampling-based methodology to investigate personalization.
result RL algorithm's personalization may be due to stochasticity.
Neural network estimates rigid motion in stroke imaging to improve image quality.
problem Rigid patient motion during C-arm CBCT imaging reduces image quality.
method Neural network trained to regress reprojection error based on image information.
result Neural network outperforms entropy-based method in motion estimation.
Self-supervised fine-tuning corrects SR CNNs for unseen models and artifacts.
problem SR CNNs' lack of robustness to unseen image formation models and generation of artifacts.
method Iterative fine-tuning using a data fidelity loss at test time.
result Successfully corrects SR solutions for unseen models and GAN artifacts.
Extended Isolation Forest improves anomaly detection by resolving score artifacts.
problem Anomaly score heat maps suffer from artifacts due to branching operation in binary trees.
method Two approaches proposed: random data transformation and random hyperplane slicing.
result Robustness improved, variance of scores along constant level sets reduced.
Improving software quality through effective organizational learning.
problem Lack of reliable quantification methods for software evolution.
method Leveraging application lifecycle management data to identify and address managerial practices.
result Effective learning from past processes improves software quality indirectly.
New neural network reduces CT radiation, works for any ROI size.
problem CT ROI reconstruction suffers from cupping artifacts and high computation.
method Two neural networks: one learns ROI-specific artifacts, the other learns DBP inversion.
result New network outperforms existing methods for any ROI size.
New method improves autofocus in CBCT scans by 93%.
problem Improper geometry information leads to misplaced signals in CBCT.
method Learning-based motion estimation combined with CBCT consistency constraint.
result Average artifact suppression of 93% achieved.
Develops a multichannel deep network for faster, artifact-free image CS.
problem Block-wise sampling artifacts in image CS with multiple sampling rates.
method Multichannel deep network for block-based image CS, removing blocking artifacts.
result Significantly outperforms state-of-the-art CS methods in objective and subjective metrics.
In this article, we analyze the microlocal properties of the linearized forward scattering operator F and the normal operator F∗F (where F∗ is the L2 adjoint of F) which arises in Synthetic Aperture Radar imaging for the common midpoint acquisition geometry. When F∗ is applied to the scattered d…
Proposes a machine learning method to evaluate creative artifacts.
problem Need for objective and automatic evaluation of creative artifacts.
method Regression-based learning framework considering novelty, influence, value, and unexpectedness.
result Promising results in predicting movie ratings and identifying creative movies.
The paper uses geometry to assess how hard examples are for NLP models.
problem Challenges in NLP datasets and classifiers, especially with shallow features.
method Information geometry to quantify example difficulty, exploring BERT, CNN, and fasttext.
result Deep learning models are vulnerable to word substitutions in difficult examples.
Develops a kernel method for computing Wasserstein distance.
problem Lack of kernel methods for nonlinear data in Wasserstein distance.
method Kernel trick to compute L2-Wasserstein distance in a kernel space.
result Kernel approach outperforms classical non-kernel methods in identifying CT slices with artifacts.