Caner Hazirbas | DeepAI

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∙ 06/20/2023

Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis

The advent of data-driven technology solutions is accompanied by an incr...

9 Maxim Maximov, et al. ∙

∙ 04/11/2023

Pinpointing Why Object Recognition Performance Degrades Across Income Levels and Geographies

Despite impressive advances in object-recognition, deep learning systems...

9 Laura Gustafson, et al. ∙

∙ 03/08/2023

The Casual Conversations v2 Dataset

This paper introduces a new large consent-driven dataset aimed at assist...

0 Bilal Porgali, et al. ∙

∙ 12/09/2022

A Whac-A-Mole Dilemma: Shortcuts Come in Multiples Where Mitigating One Amplifies Others

Machine learning models have been found to learn shortcuts – unintended ...

14 Zhiheng Li, et al. ∙

∙ 11/10/2022

Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness

Developing robust and fair AI systems require datasets with comprehensiv...

7 Caner Hazirbas, et al. ∙

∙ 11/03/2022

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations

Deep learning vision systems are widely deployed across applications whe...

2 Badr Youbi Idrissi, et al. ∙

∙ 03/31/2022

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

Our work focuses on addressing sample deficiency from low-density region...

1 Vikash Sehwag, et al. ∙

∙ 02/15/2022

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

Does everyone equally benefit from computer vision systems? Answers to t...

0 Priya Goyal, et al. ∙

∙ 11/18/2021

Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions

It is well known that many machine learning systems demonstrate bias tow...

0 Chunxi Liu, et al. ∙

∙ 06/17/2021

Localized Uncertainty Attacks

The susceptibility of deep learning models to adversarial perturbations ...

3 Ousmane Amadou Dia, et al. ∙

∙ 04/06/2021

Towards measuring fairness in AI: the Casual Conversations dataset

This paper introduces a novel dataset to help researchers evaluate their...

0 Caner Hazirbas, et al. ∙

∙ 01/19/2018

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

The finding that very large networks can be trained efficiently and reli...

0 Nikolaus Mayer, et al. ∙

∙ 04/11/2017

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

While variational methods have been among the most powerful tools for so...

0 Tim Meinhardt, et al. ∙

∙ 04/04/2017

Deep Depth From Focus

Depth from Focus (DFF) is one of the classical ill-posed inverse problem...

0 Caner Hazirbas, et al. ∙

∙ 11/23/2016

Image-based localization using LSTMs for structured feature correlation

In this work we propose a new CNN+LSTM architecture for camera pose regr...

0 Florian Walch, et al. ∙

∙ 04/26/2015

FlowNet: Learning Optical Flow with Convolutional Networks

Convolutional neural networks (CNNs) have recently been very successful ...

0 Philipp Fischer, et al. ∙

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