Search Results for author: Michal Kucer

Found 6 papers, 2 papers with code

How Robust Are Energy-Based Models Trained With Equilibrium Propagation?

no code implementations21 Jan 2024 Siddharth Mansingh, Michal Kucer, Garrett Kenyon, Juston Moore, Michael Teti

Deep neural networks (DNNs) are easily fooled by adversarial perturbations that are imperceptible to humans.

DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding

1 code implementation7 Nov 2023 Kehinde Ajayi, Xin Wei, Martin Gryder, Winston Shields, Jian Wu, Shawn M. Jones, Michal Kucer, Diane Oyen

CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents.

3D Reconstruction Benchmarking +4

Robustness to Label Noise Depends on the Shape of the Noise Distribution in Feature Space

no code implementations2 Jun 2022 Diane Oyen, Michal Kucer, Nick Hengartner, Har Simrat Singh

However, for the special case of class-dependent label noise (independent of features given the class label), the tipping point can be as low as 50%.

On visual self-supervision and its effect on model robustness

no code implementations8 Dec 2021 Michal Kucer, Diane Oyen, Garrett Kenyon

We identify primary ways in which self-supervision can be added to adversarial training, and observe that using a self-supervised loss to optimize both network parameters and find adversarial examples leads to the strongest improvement in model robustness, as this can be viewed as a form of ensemble adversarial training.

Out-of-Distribution Detection Self-Supervised Learning

Transfer learning with fewer ImageNet classes

no code implementations NeurIPS Workshop ImageNet_PPF 2021 Michal Kucer, Diane Oyen

Though much previous work tried to uncover the best practices for transfer learning, much is left unexplored.

Transfer Learning

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