Mohammad Sabokrou

Mohammad Sabokrou

Staff Research Scintist

Publications

Selected Papers

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  • He, P., Xu, H., Xing, Y., Ren, J., Cui, Y., Zeng, S., ... & Sabokrou, M. (2023). Stealthy Backdoor Attack via Confidence-driven Sampling. TMLR2024 [paper]
  • Hossein Mirzaei, Ali Ansari, Bahar Dibaei Nia, Mojtaba Nafez, Moein Madadi, Sepehr Rezaee, Zeinab Sadat Taghavi, Arad Maleki, Kian Shamsaie, Mahdi Hajialilue, Jafar Habibi, Mohammad Sabokrou, & Mohammad Hossein Rohban. Scanning Trojaned Models Using Out-of-Distribution Samples. In NeurIPS, 2024.
  • Hossein Mirzaei, Mojtaba Nafez, Mohammad Jafari, Mohammad Bagher Soltani, Mohammad Azizmalayeri, Jafar Habibi, Mohammad Hossein Rohban, Mohammad Sabokrou. (2024) Universal Novelty Detection through Adaptive Contrastive Learning. In CVPR.
  • Mozhgan Pourkeshavarz, Mohammad Sabokrou, & Amir Rasouli. (2024) Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving. In CVPR.
  • Mirzaei, H., Salehi, M., Shahabi, S., Gavves, E., Snoek, C. G. M., Sabokrou, M., Rohban, M. H. (2023). Fake it till you make it: Near-distribution novelty detection by score-based generative models. In International Conference on Learning Representations (ICLR).
  • Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M. H., Sabokrou, M. (2022). A unified survey on anomaly, novelty, open-set, and out-of-distribution detection: Solutions and future challenges. Transaction on Machine Learning Research (TMLR).
  • Pourkeshavarz, M., Zhao, G., Sabokrou, M. (2022). Looking Back on Learned Experiences For Class/task Incremental Learning. In International Conference on Learning Representations (ICLR).
  • Pourreza, M., Mohammadi, B., Khaki, M., Bouindour, S., Snoussi, H., Sabokrou, M. (2021). G2D: Generate to Detect Irregularities. In IEEE Winter Conference on Applications of Computer Vision (WACV).
  • Sabokrou, M., Khalooei, M., Adeli, E. (2019). Self-Supervised Representation Learning via Neighborhood-Relational Encoding. In IEEE International Conference on Computer Vision (ICCV).
  • Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E. (2018). End-to-End Deep One-class classification. IEEE Transaction On Neural Network and Learning Systems.
  • Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E. (2018). Adversarially Learned One-Class Classifier for Novelty Detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  • Mohammadi, B., Sabokrou, M. (2019). End-to-End Adversarial Learning for Intrusion Detection in Computer Networks. In IEEE Conference on Local Computer Networks (LCN).
  • Sharifipour, S., Fayyazi, H., Sabokrou, M., Adeli, E. (2019). Unsupervised Feature Ranking and Selection based on Autoencoders. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
  • Sabokrou, M., Fayyaz, M., Fathy, M., Moayed, Z., Klette, R. (2018). Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes. Computer Vision and Image Understanding.
  • Sabokrou, M., Pourreza, M., Fayyaz, M., Entezari, R., Fathy, M., Gall, J., Adeli, E. (2018). AVID: Adversarial Visual Irregularity Detection. Asian Conference on Computer Vision (ACCV), Perth, Australia.
  • Sabokrou, M., Fayyaz, M., Fathy, M., Klette, R. (2017). Deepcascade: Cascading 3D Deep Neural Networks for Fast Anomaly Detection and Localization in Crowded Scenes. IEEE Transactions on Image Processing, 26(4), 1992-2004.