Nathan Drenkow | DeepAI

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∙ 08/28/2023

RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning

Object-centric representation learning offers the potential to overcome ...

0 Nathan Drenkow, et al. ∙

∙ 05/26/2023

Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence

Despite the progress in deep learning networks, efficient learning at th...

0 Erik C. Johnson, et al. ∙

∙ 04/06/2023

Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models

To safely deploy deep learning-based computer vision models for computer...

11 Mitchell Pavlak, et al. ∙

∙ 11/28/2022

Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity

The deployment of machine learning models in safety-critical application...

0 Nathan Drenkow, et al. ∙

∙ 12/01/2021

Robustness in Deep Learning for Computer Vision: Mind the gap?

Deep neural networks for computer vision tasks are deployed in increasin...

12 Nathan Drenkow, et al. ∙

∙ 09/13/2021

On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation

Scene depth estimation from stereo and monocular imagery is critical for...

22 Zhaoshuo Li, et al. ∙

∙ 08/16/2021

Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?

Perturbation-based attacks, while not physically realizable, have been t...

0 Max Lennon, et al. ∙

∙ 12/11/2020

Objectness-Guided Open Set Visual Search and Closed Set Detection

Searching for small objects in large images is currently challenging for...

0 Nathan Drenkow, et al. ∙

∙ 12/11/2020

Random Projections for Adversarial Attack Detection

Whilst adversarial attack detection has received considerable attention,...

0 Nathan Drenkow, et al. ∙

∙ 05/01/2020

Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks

We focus on the development of effective adversarial patch attacks and –...

0 Neil Fendley, et al. ∙

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