Search Results for author: Hossein Souri

Found 13 papers, 4 papers with code

Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks

2 code implementations NeurIPS 2023 Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein

Battle of the Backbones (BoB) makes this choice easier by benchmarking a diverse suite of pretrained models, including vision-language models, those trained via self-supervised learning, and the Stable Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more.

Benchmarking object-detection +2

Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning

1 code implementation17 Oct 2022 Yuxin Wen, Jonas Geiping, Liam Fowl, Hossein Souri, Rama Chellappa, Micah Goldblum, Tom Goldstein

Federated learning is particularly susceptible to model poisoning and backdoor attacks because individual users have direct control over the training data and model updates.

Federated Learning Image Classification +2

Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors

1 code implementation20 May 2022 Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann Lecun, Andrew Gordon Wilson

Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task.

Transfer Learning

A Deep Dive into Dataset Imbalance and Bias in Face Identification

no code implementations15 Mar 2022 Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, Micah Goldblum, Tom Goldstein

This is an unfortunate omission, as 'imbalance' is a more complex matter in identification; imbalance may arise in not only the training data, but also the testing data, and furthermore may affect the proportion of identities belonging to each demographic group or the number of images belonging to each identity.

Face Identification Face Recognition +1

Identification of Attack-Specific Signatures in Adversarial Examples

no code implementations13 Oct 2021 Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, Rama Chellappa

The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks.

Adversarial Attack

Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch

1 code implementation16 Jun 2021 Hossein Souri, Liam Fowl, Rama Chellappa, Micah Goldblum, Tom Goldstein

In contrast, the Hidden Trigger Backdoor Attack achieves poisoning without placing a trigger into the training data at all.

Backdoor Attack

Certified Watermarks for Neural Networks

no code implementations1 Jan 2021 Arpit Amit Bansal, Ping-Yeh Chiang, Michael Curry, Hossein Souri, Rama Chellappa, John P Dickerson, Rajiv Jain, Tom Goldstein

Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.

GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue

no code implementations24 Sep 2020 Pirazh Khorramshahi, Hossein Souri, Rama Chellappa, Soheil Feizi

To tackle this issue, we take an information-theoretic approach and maximize a variational lower bound on the entropy of the generated samples to increase their diversity.

Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition

no code implementations14 Jun 2020 Prithviraj Dhar, Joshua Gleason, Hossein Souri, Carlos D. Castillo, Rama Chellappa

Therefore, we present a novel `Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors obtained from previously trained face recognition networks.

Face Recognition Face Verification +1

ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence

no code implementations7 Oct 2019 Chun Pong Lau, Hossein Souri, Rama Chellappa

To mitigate the degradation due to turbulence which includes deformation and blur, we propose a generative single frame restoration algorithm which disentangles the blur and deformation due to turbulence and reconstructs a restored image.

Disentanglement Face Recognition +1

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