Papers Noise Estimation
“Noise Estimation” 태그가 달린 논문 136편 · 필터 해제
Robust Trajectory Distillation: Hybrid Reweighting Meets Teacher-Inspired Targets
Dataset distillation (DD) condenses large corpora into compact, information-rich subsets for efficient training and reuse. However, under noisy supervision, DD risks condensing corrupted associations together with useful…
Noise EstimationBEV-Denoise: Learning Intrinsic Noise for Accurate Bird's-Eye-View Semantic Segmentation
In this paper, we present a framework dubbed \textbf{BEV-Denoise} that estimates and removes intrinsic noise from learned Bird's-Eye-View (BEV) features to achieve accurate BEV semantic segmentation. Inspired by the nois…
Semantic SegmentationNoise EstimationRealistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning
Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In supervised machine learning frameworks trained on simulated data, discrepa…
Noise EstimationWhere LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic content, such as paper abstracts, web pages, or product descriptions, la…
Node ClassificationNoise EstimationGraph LearningOutlier-Robust Diffusion Solvers for Inverse Problems
Methods based on diffusion models (DMs) for solving inverse problems (IPs) have recently achieved remarkable performance. However, DM-based methods typically struggle against outliers, which are common in real-world meas…
Noise EstimationBreaking Watermarks in the Frequency Domain: A Modulated Diffusion Attack Framework
Digital image watermarking has advanced rapidly for copyright protection of generative AI, yet the comparatively limited progress in watermark attack techniques has broken the attack-defense balance and hindered further …
Noise EstimationBeyond Fixed Inference: Quantitative Flow Matching for Adaptive Image Denoising
Diffusion and flow-based generative models have shown strong potential for image restoration. However, image denoising under unknown and varying noise conditions remains challenging, because the learned vector fields may…
Image RestorationNoise EstimationImage DenoisingNeural Aided Adaptive Innovation-Based Invariant Kalman Filter
Autonomous platforms require accurate positioning to complete their tasks. To this end, a Kalman filter-based algorithms, such as the extended Kalman filter or invariant Kalman filter, utilizing inertial and external sen…
Domain AdaptationNoise EstimationData-Centric Benchmark for Label Noise Estimation and Ranking in Remote Sensing Binary Building Segmentation
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and often affected by noise due to the labor-intensive and time-con…
The Semantic Segmentation Of Remote Sensing ImageryImage SegmentationNoise EstimationEnhancing Generalization in Evolutionary Feature Construction for Symbolic Regression through Vicinal Jensen Gap Minimization
Genetic programming-based feature construction has achieved significant success in recent years as an automated machine learning technique to enhance learning performance. However, overfitting remains a challenge that li…
Intrusion DetectionData AugmentationNoise EstimationGradient-Aligned Calibration for Post-Training Quantization of Diffusion Models
Diffusion models have shown remarkable performance in image synthesis by progressively estimating a smooth transition from a Gaussian distribution of noise to a real image. Unfortunately, their practical deployment is li…
Noise EstimationGeneralization Bounds for Transformer Channel Decoders
Transformer channel decoders, such as the Error Correction Code Transformer (ECCT), have shown strong empirical performance in channel decoding, yet their generalization behavior remains theoretically unclear. This paper…
Noise EstimationSURE Guided Posterior Sampling: Trajectory Correction for Diffusion-Based Inverse Problems
Diffusion models have emerged as powerful learned priors for solving inverse problems. However, current iterative solving approaches which alternate between diffusion sampling and data consistency steps typically require…
Noise EstimationImage Denoising Using Global and Local Circulant Representation
The proliferation of imaging devices and countless image data generated every day impose an increasingly high demand on efficient and effective image denoising. In this paper, we establish a theoretical connection betwee…
Noise EstimationImage DenoisingAnimus3D: Text-driven 3D Animation via Motion Score Distillation
We present Animus3D, a text-driven 3D animation framework that generates motion field given a static 3D asset and text prompt. Previous methods mostly leverage the vanilla Score Distillation Sampling (SDS) objective to d…
Noise EstimationBring Your Dreams to Life: Continual Text-to-Video Customization
Customized text-to-video generation (CTVG) has recently witnessed great progress in generating tailored videos from user-specific text. However, most CTVG methods assume that personalized concepts remain static and do no…
Text-to-Video GenerationNoise EstimationLearning stochasticity: a nonparametric framework for intrinsic noise estimation
Understanding the principles that govern dynamical systems is a central challenge across many scientific domains, including biology and ecology. Incomplete knowledge of nonlinear interactions and stochastic effects often…
Noise EstimationSpectral Compressive Imaging via Chromaticity-Intensity Decomposition
In coded aperture snapshot spectral imaging (CASSI), the captured measurement entangles spatial and spectral information, posing a severely ill-posed inverse problem for hyperspectral images (HSIs) reconstruction. Moreov…
Noise EstimationEfficient Image Denoising Using Global and Local Circulant Representation
The advancement of imaging devices and countless image data generated everyday impose an increasingly high demand on efficient and effective image denoising. In this paper, we present a computationally simple denoising a…
Noise EstimationImage DenoisingNoise-adapted Neural Operator for Robust Non-Line-of-Sight Imaging
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Computational imaging, especially non-line-of-sight (N…
Image ReconstructionNoise Estimation