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Found 7 papers, 4 papers with code
Yes, IoU loss is submodular - as a function of the mispredictions
This note is a response to [7] in which it is claimed that [13, Proposition 11] is false.
The Lovász-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks
The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses.
Function Norms and Regularization in Deep Networks
Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems.
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses.
Ranked #34 on
Semantic Segmentation
on PASCAL VOC 2012 test
Encoder Based Lifelong Learning
This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks.
Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
The use of a function norm introduces a direct control over the complexity of the function with the aim of diminishing the risk of overfitting.
Stochastic Function Norm Regularization of Deep Networks
In this paper, we study the feasibility of directly using the $L_2$ function norm for regularization.
