Chaithanya Kumar Mummadi | DeepAI

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∙ 08/02/2023

More Context, Less Distraction: Visual Classification by Inferring and Conditioning on Contextual Attributes

CLIP, as a foundational vision language model, is widely used in zero-sh...

0 Bang An, et al. ∙

∙ 06/22/2023

Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches

Certifiably robust defenses against adversarial patches for image classi...

0 Aniruddha Saha, et al. ∙

∙ 08/12/2021

DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities

Common deep neural networks (DNNs) for image classification have been sh...

1 Elias Eulig, et al. ∙

∙ 06/28/2021

Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation

Deep neural networks often exhibit poor performance on data that is unli...

0 Chaithanya Kumar Mummadi, et al. ∙

∙ 04/20/2021

Does enhanced shape bias improve neural network robustness to common corruptions?

Convolutional neural networks (CNNs) learn to extract representations of...

0 Chaithanya Kumar Mummadi, et al. ∙

∙ 10/04/2019

SELF: Learning to Filter Noisy Labels with Self-Ensembling

Deep neural networks (DNNs) have been shown to over-fit a dataset when b...

0 Duc Tam Nguyen, et al. ∙

∙ 08/09/2019

Group Pruning using a Bounded-Lp norm for Group Gating and Regularization

Deep neural networks achieve state-of-the-art results on several tasks w...

0 Chaithanya Kumar Mummadi, et al. ∙

∙ 12/10/2018

Defending against Universal Perturbations with Shared Adversarial Training

Classifiers such as deep neural networks have been shown to be vulnerabl...

12 Chaithanya Kumar Mummadi, et al. ∙

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