1 code implementation • 26 Feb 2024 • Pau de Jorge, Riccardo Volpi, Puneet K. Dokania, Philip H. S. Torr, Gregory Rogez
In our experiments, we present different anomaly segmentation datasets based on POC-generated data and show that POC can improve the performance of recent state-of-the-art anomaly fine-tuning methods in several standardized benchmarks.
1 code implementation • 26 Oct 2023 • Jindong Gu, Xiaojun Jia, Pau de Jorge, Wenqain Yu, Xinwei Liu, Avery Ma, Yuan Xun, Anjun Hu, Ashkan Khakzar, Zhijiang Li, Xiaochun Cao, Philip Torr
This survey explores the landscape of the adversarial transferability of adversarial examples.
1 code implementation • CVPR 2023 • Pau de Jorge, Riccardo Volpi, Philip Torr, Gregory Rogez
We analyze a broad variety of models, spanning from older ResNet-based architectures to novel transformers and assess their reliability based on four metrics: robustness, calibration, misclassification detection and out-of-distribution (OOD) detection.
1 code implementation • 16 Jun 2022 • Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal, Adel Bibi, Puneet K. Dokania, Pascal Frossard, Gregory Rogéz, Philip H. S. Torr
Through extensive experiments we analyze this novel phenomenon and discover that the presence of these easy features induces a learning shortcut that leads to CO. Our findings provide new insights into the mechanisms of CO and improve our understanding of the dynamics of AT.
1 code implementation • CVPR 2022 • Riccardo Volpi, Pau de Jorge, Diane Larlus, Gabriela Csurka
We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation.
1 code implementation • 2 Feb 2022 • Pau de Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip H. S. Torr, Grégory Rogez, Puneet K. Dokania
Recently, Wong et al. showed that adversarial training with single-step FGSM leads to a characteristic failure mode named Catastrophic Overfitting (CO), in which a model becomes suddenly vulnerable to multi-step attacks.
no code implementations • 29 Sep 2021 • Pau de Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip Torr, Grégory Rogez, Puneet K. Dokania
In this work, we methodically revisit the role of noise and clipping in single-step adversarial training.
1 code implementation • ICLR 2021 • Pau de Jorge, Amartya Sanyal, Harkirat S. Behl, Philip H. S. Torr, Gregory Rogez, Puneet K. Dokania
Recent studies have shown that skeletonization (pruning parameters) of networks \textit{at initialization} provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance.