2 citations · 2 across the 3 of their papers we have counts for
7 papers · 1 filter
Binarized Weight Error Networks With a Transition Regularization Term
Savas Ozkan, Gozde Bozdagi Akar
This paper proposes a novel binarized weight network (BT) for a resource-efficient neural structure. The proposed model estimates a binary representation of weights by taking into…
Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss
Savas Ozkan, Gozde Bozdagi Akar
This study proposes a novel framework for spectral unmixing by using 1D convolution kernels and spectral uncertainty. High-level representations are computed from data, and they ar…
Automatic Liver Segmentation with Adversarial Loss and Convolutional Neural Network
Bora Baydar, Savas Ozkan, Gozde Bozdagi Akar
Automatic segmentation of medical images is among most demanded works in the medical information field since it saves time of the experts in the field and avoids human error factor…
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty
Savas Ozkan, Gozde Bozdagi Akar
In this study, we propose a novel framework for hyperspectral unmixing by using an improved deep spectral convolution network (DSCN++) combined with endmember uncertainty. DSCN++ i…
Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search
Savas Ozkan, Gozde Bozdagi Akar
The cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardw…
Deep Spectral Convolution Network for HyperSpectral Unmixing
Savas Ozkan, Gozde Bozdagi Akar
In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented thr…