215 citations · 231 across the 6 of their papers we have counts for
6 papers
Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union
Zifu Wang, Maxim Berman, Amal Rannen-Triki +6
Semantic segmentation datasets often exhibit two types of imbalance: \textit{class imbalance}, where some classes appear more frequently than others and \textit{size imbalance}, wh…
Yes, IoU loss is submodular - as a function of the mispredictions
Maxim Berman, Matthew B. Blaschko, Amal Rannen Triki +1
This note is a response to [7] in which it is claimed that [13, Proposition 11] is false. We demonstrate here that this assertion in [7] is false, and is based on a misreading of t…
Function Norms and Regularization in Deep Networks
Amal Rannen Triki, Maxim Berman, Matthew B. Blaschko
Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems. However, regularization is generally achiev…
Encoder Based Lifelong Learning
Amal Rannen Triki, Rahaf Aljundi, Mathew B. Blaschko +1
This paper introduces a new lifelong learning solution where a single model is trained for a sequence of tasks. The main challenge that vision systems face in this context is catas…
Intraoperative margin assessment of human breast tissue in optical coherence tomography images using deep neural networks
Amal Rannen Triki, Matthew B. Blaschko, Yoon Mo Jung +4
Objective: In this work, we perform margin assessment of human breast tissue from optical coherence tomography (OCT) images using deep neural networks (DNNs). This work simulates a…
Stochastic Function Norm Regularization of Deep Networks
Amal Rannen Triki, Matthew B. Blaschko
Deep neural networks have had an enormous impact on image analysis. State-of-the-art training methods, based on weight decay and DropOut, result in impressive performance when a ve…