Search Results for author: Gozde Unal

Found 15 papers, 6 papers with code

Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation

1 code implementation6 Aug 2021 Ufuk Demir, Atahan Ozer, Yusuf H. Sahin, Gozde Unal

However, there are two drawbacks of the approach: most of the edges in the graph are assigned randomly and the GCN is trained independently from the segmentation network.

Computed Tomography (CT) Graph Convolutional Network +1

Evidential Turing Processes

1 code implementation2 Jun 2021 Melih Kandemir, Abdullah Akgül, Manuel Haussmann, Gozde Unal

A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e. g.\ class overlap), and iii) accurately identifies queries coming out of the target domain and reject them.

Image Classification

Single Image Depth Estimation: An Overview

no code implementations13 Apr 2021 Alican Mertan, Damien Jade Duff, Gozde Unal

We review solutions to the problem of depth estimation, arguably the most important subtask in scene understanding.

Depth Estimation Scene Understanding +1

ODFNet: Using orientation distribution functions to characterize 3D point clouds

no code implementations8 Dec 2020 Yusuf H. Sahin, Alican Mertan, Gozde Unal

Learning new representations of 3D point clouds is an active research area in 3D vision, as the order-invariant point cloud structure still presents challenges to the design of neural network architectures.

Exploring DeshuffleGANs in Self-Supervised Generative Adversarial Networks

1 code implementation3 Nov 2020 Gulcin Baykal, Furkan Ozcelik, Gozde Unal

Lastly, we show the contribution of the self-supervision tasks to the GAN training on the loss landscape and present that the effects of these tasks may not be cooperative to the adversarial training in some settings.

Image Generation

Relative Depth Estimation as a Ranking Problem

no code implementations14 Oct 2020 Alican Mertan, Damien Jade Duff, Gozde Unal

To this end, we have introduced a listwise ranking loss borrowed from ranking literature, weighted ListMLE, to the relative depth estimation problem.

Depth Estimation

EfficientSeg: An Efficient Semantic Segmentation Network

1 code implementation14 Sep 2020 Vahit Bugra Yesilkaynak, Yusuf H. Sahin, Gozde Unal

Deep neural network training without pre-trained weights and few data is shown to need more training iterations.

Semantic Segmentation

Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs

1 code implementation30 Jun 2020 Furkan Ozcelik, Ugur Alganci, Elif Sertel, Gozde Unal

Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years.

Colorization Self-Supervised Learning +1

DeshuffleGAN: A Self-Supervised GAN to Improve Structure Learning

1 code implementation15 Jun 2020 Gulcin Baykal, Gozde Unal

Generative Adversarial Networks (GANs) triggered an increased interest in problem of image generation due to their improved output image quality and versatility for expansion towards new methods.

Image Generation

Medical Imaging with Deep Learning: MIDL 2019 -- Extended Abstract Track

no code implementations21 May 2019 M. Jorge Cardoso, Aasa Feragen, Ben Glocker, Ender Konukoglu, Ipek Oguz, Gozde Unal, Tom Vercauteren

This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019.

Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI

no code implementations12 Apr 2018 Sahin Olut, Yusuf Huseyin Sahin, Ugur Demir, Gozde Unal

To that end, we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term.

Image Generation

Patch-Based Image Inpainting with Generative Adversarial Networks

no code implementations20 Mar 2018 Ugur Demir, Gozde Unal

Area of image inpainting over relatively large missing regions recently advanced substantially through adaptation of dedicated deep neural networks.

Image Inpainting

Deep Stacked Networks with Residual Polishing for Image Inpainting

no code implementations31 Dec 2017 Ugur Demir, Gozde Unal

Then the second network modifies the repaired image to cancel the noise introduced by the first network.

Denoising Image Inpainting

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