AIConfPaper
← Search

Mohammad Jafari

5 accepted papers

2024

Killing It With Zero-Shot: Adversarially Robust Novelty Detection

Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Zeinab Sadat Taghavi, Mohammad Sabokrou, Mohammad Hossein Rohban
ICASSP 2024accepted

Novelty Detection (ND) plays a crucial role in machine learning by identifying new or unseen data during model inference. This capability is especially important for the safe and reliable operation of automated systems. Despite advances in this field, existing techniques often fail to maintain their…

Cited by 0Source ↗Scholar ↗
2024

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Ali Ansari, Sepehr Ghobadi, Masoud Hadi +4
ICML 2024poster

In recent years, there have been significant improvements in various forms of image outlier detection. However, outlier detection performance under adversarial settings lags far behind that in standard settings. This is due to the lack of effective exposure to adversarial scenarios during training,…

Cited by 8Source ↗PDF ↗code ↗Scholar ↗
2024

The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset Selection

Mohammad Jafari, Yimeng Zhang, Yihua Zhang, Sijia Liu
ICASSP 2024accepted

As machine learning tasks continue to evolve, the trend has been to gather larger datasets and train increasingly larger models. While this has led to advancements in accuracy, it has also escalated computational costs to unsustainable levels. Addressing this, our work aims to strike a delicate bala…

Cited by 0Source ↗Scholar ↗
2024

Universal Novelty Detection Through Adaptive Contrastive Learning

Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari, Mohammad Bagher Soltani, Mohammad Azizmalayeri, Jafar Habibi +2
CVPR 2024poster

Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality which can be interpreted as generalization across various distributions of training or test data. More precisely for novelty detection distrib…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2021

Reciprocal Landmark Detection and Tracking With Extremely Few Annotations

Jianzhe Lin, Ghazal Sahebzamani, Christina Luong, Fatemeh Taheri Dezaki, Mohammad Jafari, Purang Abolmaesumi +1
CVPR 2021poster

Localization of anatomical landmarks to perform two-dimensional measurements in echocardiography is part of routine clinical workflow in cardiac disease diagnosis. Automatic localization of those landmarks is highly desirable to improve workflow and reduce interobserver variability. Training a machi…

Cited by 9PDF ↗Scholar ↗