Certification of Model Robustness in Active Class Selection

2021 ◽  
pp. 266-281
Author(s):  
Mirko Bunse ◽  
Katharina Morik
Keyword(s):  
Model Robustness ◽  
Active Class

scholarly journals Multi-Feature Guided Low-Light Image Enhancement

2021 ◽  
Vol 11 (11) ◽  
pp. 5055
Author(s):  
Hong Liang ◽  
Ankang Yu ◽  
Mingwen Shao ◽  
Yuru Tian
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Low Light ◽  
Low Contrast ◽  
Light Image ◽  
Affine Model ◽  
The Neural Network ◽  
Low Visibility ◽  
Model Robustness ◽  
Traditional Image

Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.

scholarly journals A study of real-world micrograph data quality and machine learning model robustness

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xiaoting Zhong ◽  
Brian Gallagher ◽  
Keenan Eves ◽  
Emily Robertson ◽  
T. Nathan Mundhenk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real World ◽  
Image Feature ◽  
Molecular Solids ◽  
Pixel Intensity ◽  
Machine Learning Model ◽  
Model Predictions ◽  
Intensity Normalization ◽  
Model Robustness

AbstractMachine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

scholarly journals Evaluating Deception Detection Model Robustness To Linguistic Variation

2021 ◽  
Author(s):  
Maria Glenski ◽  
Ellyn Ayton ◽  
Robin Cosbey ◽  
Dustin Arendt ◽  
Svitlana Volkova
Keyword(s):  
Deception Detection ◽  
Linguistic Variation ◽  
Detection Model ◽  
Model Robustness

scholarly journals Technical note: PMR – a proxy metric to assess hydrological model robustness in a changing climate

2021 ◽  
Author(s):  
Paul Royer-Gaspard ◽  
Vazken Andréassian ◽  
Guillaume Thirel
Keyword(s):  
Hydrological Model ◽  
Evaluation Studies ◽  
Computational Cost ◽  
Climatic Conditions ◽  
Technical Note ◽  
Performance Metric ◽  
Reliable Assessment ◽  
Sample Test ◽  
The Impact ◽  
Model Robustness

Abstract. The ability of hydrological models to perform in climatic conditions different from those encountered in calibration is crucial to ensure a reliable assessment of the impact of climate change in water management sectors. However, most evaluation studies based on the Differential Split-Sample Test (DSST) endorsed the consensus that rainfall-runoff models lack climatic robustness. Models typically exhibit substantial errors on streamflow volumes applied under climatologically different conditions. In this technical note, we propose a new performance metric to evaluate model robustness without applying the DSST and which performs with a single hydrological model calibration. The Proxy for Model Robustness (PMR) is based on the systematic computation of model error on sliding sub-periods of the whole streamflow time series. We demonstrate that the metric shows patterns similar to those obtained with the DSST for a conceptual model on a set of 377 French catchments. An analysis of sensitivity to the length of the sub-periods shows that this length influences the values of the PMR and its adequation with DSST biases. We recommend a range of a few years for the choice of sub-period lengths, although this should be context-dependent. Our work makes it possible to evaluate the temporal transferability of any hydrological model, including uncalibrated models, at a very low computational cost.

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