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Found 15 papers, 4 papers with code
Learning to Segment from Noisy Annotations: A Spatial Correction Approach
However, segmentation label noise usually has strong spatial correlation and has prominent bias in distribution.
AI pipeline for accurate retinal layer segmentation using OCT 3D images
A simple-to-implement analytical equation is shown to be working for brightness manipulation with a 1% increment in mean pixel values and a 77% decrease in the number of zeros.
Characterization of 3D Printers and X-Ray Computerized Tomography
It is found that ProJet MJP gives the best quality of printed samples with the least amount of surface roughness and almost near to the actual porosity value.
Noise analysis, error estimates, and Gamma Radiation Measurement for limited detector computerized tomography application
The analysis shows that measurement data with normal distribution inflicts the least noise in inverse recovery.
A Manifold View of Adversarial Risk
Assuming data lies in a manifold, we investigate two new types of adversarial risk, the normal adversarial risk due to perturbation along normal direction, and the in-manifold adversarial risk due to perturbation within the manifold.
AI and conventional methods for UCT projection data estimation
Other performance indices show that FFT method is processing the UCT signal with best recovery.
Deep Learning models for benign and malign Ocular Tumor Growth Estimation
U-net with UVgg16 is best for malign tumor data set with treatment (having considerable variation) and U-net with Inception backbone for benign tumor data (with minor variation).
AI Algorithm for Mode Classification of PCF SPR Sensor Design
For this design algorithm has selected Support Vector Machine (SVM) as the best option with an accuracy of 96%, F1-Score is 95. 83%, and MCC of 92. 30%.
Topological Detection of Trojaned Neural Networks
Deep neural networks are known to have security issues.
Learning with Feature-Dependent Label Noise: A Progressive Approach
Label noise is frequently observed in real-world large-scale datasets.
Ranked #12 on
Learning with noisy labels
on ANIMAL
Stability of SGD: Tightness Analysis and Improved Bounds
This raises the question: is the stability analysis of [18] tight for smooth functions, and if not, for what kind of loss functions and data distributions can the stability analysis be improved?
Revisiting the Stability of Stochastic Gradient Descent: A Tightness Analysis
We provide empirical evidence that this condition holds for several loss functions, and provide theoretical evidence that the known tight SGD stability bounds for convex and non-convex loss functions can be circumvented by HC loss functions, thus partially explaining the generalization of deep neural networks.
A Topological Filter for Learning with Label Noise
Noisy labels can impair the performance of deep neural networks.
Error-Bounded Correction of Noisy Labels
To be robust against label noise, many successful methods rely on the noisy classifiers (i. e., models trained on the noisy training data) to determine whether a label is trustworthy.
Ranked #40 on
Image Classification
on Clothing1M
Label Cleaning with Likelihood Ratio Test
To collect large scale annotated data, it is inevitable to introduce label noise, i. e., incorrect class labels.
