SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling

no code implementations • 25 Nov 2023 • Habib Hajimolahoseini, Omar Mohamed Awad, Walid Ahmed, Austin Wen, Saina Asani, Mohammad Hassanpour, Farnoosh Javadi, Mehdi Ahmadi, Foozhan Ataiefard, Kangling Liu, Yang Liu

In this paper, we present SwiftLearn, a data-efficient approach to accelerate training of deep learning models using a subset of data samples selected during the warm-up stages of training.

Reproducing AmbientGAN: Generative models from lossy measurements

1 code implementation • 23 Oct 2018 • Mehdi Ahmadi, Timothy Nest, Mostafa Abdelnaim, Thanh-Dung Le

The results shown by \citep{bora2018ambientgan} are quite promising for the problem of incomplete data, and have potentially important implications for generative approaches to compressed sensing and ill-posed problems.

Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images

no code implementations • 20 Sep 2018 • Zahra Sobhaninia, Safiyeh Rezaei, Alireza Noroozi, Mehdi Ahmadi, Hamidreza Zarrabi, Nader Karimi, Ali Emami, Shadrokh Samavi

The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network.

Brain Tumor Segmentation Segmentation +1