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Loss function

Known as: Zero-one loss, Loss, Risk function 
In mathematical optimization, statistics, decision theory and machine learning, a loss function or cost function is a function that maps an event or… 
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Related topics

Related topics

50 relations
Adaptive filterBackpropagationBootstrapping (statistics)Constrained optimization

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2020
Review
2020
A survey of loss functions for semantic segmentation
Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease… 
Highly Cited
2017
Highly Cited
2017
Loss Functions for Image Restoration With Neural Networks
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have… 
Highly Cited
2017
Highly Cited
2017
Focal Loss for Dense Object Detection
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is… 
Highly Cited
2017
Highly Cited
2017
Robust Loss Functions under Label Noise for Deep Neural Networks
In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge… 
Highly Cited
2017
Highly Cited
2017
Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks
Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main… 
Highly Cited
2016
Highly Cited
2016
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of… 
Highly Cited
2015
Highly Cited
2015
You Only Look Once: Unified, Real-Time Object Detection
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection… 
Highly Cited
1997
Highly Cited
1997
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means… 
Highly Cited
1996
Highly Cited
1996
Bias Plus Variance Decomposition for Zero-One Loss Functions
We present a bias variance decomposition of expected misclassi cation rate the most commonly used loss function in supervised… 
Highly Cited
1981
Highly Cited
1981
Pattern Recognition with Fuzzy Objective Function Algorithms
  • J. Bezdek
  • Advanced Applications in Pattern Recognition
  • 1981
  • Corpus ID: 30806637
New updated! The latest book from a very famous author finally comes out. Book of pattern recognition with fuzzy objective… 
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