4 code implementations • 29 Aug 2023 • Mang Ning, Mingxiao Li, Jianlin Su, Albert Ali Salah, Itir Onal Ertugrul
In this paper, we systematically investigate the exposure bias problem in diffusion models by first analytically modelling the sampling distribution, based on which we then attribute the prediction error at each sampling step as the root cause of the exposure bias issue.
Ranked #9 on Image Generation on CIFAR-10
1 code implementation • 27 Oct 2022 • Giacomo Fiorentini, Itir Onal Ertugrul, Albert Ali Salah
Vision transformers are a top-performing architecture in computer vision, with little research on their use for pain detection.
no code implementations • 21 Oct 2020 • Koichiro Niinuma, Itir Onal Ertugrul, Jeffrey F Cohn, László A Jeni
Critical obstacles in training classifiers to detect facial actions are the limited sizes of annotated video databases and the relatively low frequencies of occurrence of many actions.
1 code implementation • 22 Jul 2018 • Baran Baris Kivilcim, Itir Onal Ertugrul, Fatos T. Yarman Vural
We observe that both undirected and directed brain networks surpass the performances of the network models used in the fMRI literature.
no code implementations • 17 Oct 2016 • Itir Onal Ertugrul, Mete Ozay, Fatos T. Yarman Vural
In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher Vectors (FV), Vector of Locally Aggregated Descriptors (VLAD) and Bag-of-Words (BoW) methods.
no code implementations • 12 Jul 2016 • Itir Onal Ertugrul, Mete Ozay, Fatos Tunay Yarman Vural
We propose a new framework, called Hierarchical Multi-resolution Mesh Networks (HMMNs), which establishes a set of brain networks at multiple time resolutions of fMRI signal to represent the underlying cognitive process.