Search Results for author: Navid Kardan

Found 8 papers, 4 papers with code

Dual Student Networks for Data-Free Model Stealing

no code implementations18 Sep 2023 James Beetham, Navid Kardan, Ajmal Mian, Mubarak Shah

To this end, the two main challenges are estimating gradients of the target model without access to its parameters, and generating a diverse set of training samples that thoroughly explores the input space.

CDFSL-V: Cross-Domain Few-Shot Learning for Videos

1 code implementation ICCV 2023 Sarinda Samarasinghe, Mamshad Nayeem Rizve, Navid Kardan, Mubarak Shah

To address this issue, in this work, we propose a novel cross-domain few-shot video action recognition method that leverages self-supervised learning and curriculum learning to balance the information from the source and target domains.

cross-domain few-shot learning Few-Shot action recognition +3

Towards Realistic Semi-Supervised Learning

1 code implementation5 Jul 2022 Mamshad Nayeem Rizve, Navid Kardan, Mubarak Shah

We also highlight the flexibility of our approach in solving novel class discovery task, demonstrate its stability in dealing with imbalanced data, and complement our approach with a technique to estimate the number of novel classes

Novel Class Discovery Open-World Semi-Supervised Learning +1

OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning

1 code implementation5 Jul 2022 Mamshad Nayeem Rizve, Navid Kardan, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah

In the open-world SSL problem, the objective is to recognize samples of known classes, and simultaneously detect and cluster samples belonging to novel classes present in unlabeled data.

Open-World Semi-Supervised Learning

Advances in adversarial attacks and defenses in computer vision: A survey

no code implementations1 Aug 2021 Naveed Akhtar, Ajmal Mian, Navid Kardan, Mubarak Shah

In [2], we reviewed the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses) until the advent of year 2018.

Towards Consistent Predictive Confidence through Fitted Ensembles

no code implementations22 Jun 2021 Navid Kardan, Ankit Sharma, Kenneth O. Stanley

Furthermore, we present a new strong baseline for more consistent predictive confidence in deep models, called fitted ensembles, where overconfident predictions are rectified by transformed versions of the original classification task.

Out of Distribution (OOD) Detection

Fitted Learning: Models with Awareness of their Limits

1 code implementation7 Sep 2016 Navid Kardan, Kenneth O. Stanley

Though deep learning has pushed the boundaries of classification forward, in recent years hints of the limits of standard classification have begun to emerge.

Classification General Classification

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