Search Results for author: Caner Hazirbas

Found 9 papers, 4 papers with code

Generating High Fidelity Data from Low-density Regions using Diffusion Models

no code implementations CVPR 2022 Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, Cristian Canton Ferrer

We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold.

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

1 code implementation15 Feb 2022 Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun, Nicolas Usunier

Systematic diagnosis of fairness, harms, and biases of computer vision systems is an important step towards building socially responsible systems.

Computer Vision Fairness

Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions

no code implementations18 Nov 2021 Chunxi Liu, Michael Picheny, Leda Sari, Pooja Chitkara, Alex Xiao, Xiaohui Zhang, Mark Chou, Andres Alvarado, Caner Hazirbas, Yatharth Saraf

This paper presents initial Speech Recognition results on "Casual Conversations" -- a publicly released 846 hour corpus designed to help researchers evaluate their computer vision and audio models for accuracy across a diverse set of metadata, including age, gender, and skin tone.

Automatic Speech Recognition Computer Vision +2

Localized Uncertainty Attacks

no code implementations17 Jun 2021 Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas, Cristian Canton Ferrer, Ilknur Kaynar Kabul, Erik Meijer

Under this threat model, we create adversarial examples by perturbing only regions in the inputs where a classifier is uncertain.

What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?

1 code implementation19 Jan 2018 Nikolaus Mayer, Eddy Ilg, Philipp Fischer, Caner Hazirbas, Daniel Cremers, Alexey Dosovitskiy, Thomas Brox

The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations.

Computer Vision Optical Flow Estimation

Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

1 code implementation ICCV 2017 Tim Meinhardt, Michael Moeller, Caner Hazirbas, Daniel Cremers

While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks.

Demosaicking Denoising +1

Deep Depth From Focus

5 code implementations4 Apr 2017 Caner Hazirbas, Sebastian Georg Soyer, Maximilian Christian Staab, Laura Leal-Taixé, Daniel Cremers

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision.

Computer Vision

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