| Understanding individual decisions of cnns via contrastive backpropagation J Gu, Y Yang, V Tresp Proceedings of the Asian Conference on Computer Vision (ACCV), 119-134, 2018 | 96 | 2018 |
| Improving the robustness of capsule networks to image affine transformations J Gu, V Tresp IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7285-7293, 2020 | 49 | 2020 |
| Are vision transformers robust to patch perturbations? J Gu, V Tresp, Y Qin European Conference on Computer Vision (ECCV), 404-421, 2022 | 40 | 2022 |
| Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness J Gu, H Zhao, V Tresp, PHS Torr European Conference on Computer Vision (ECCV), 308-325, 2022 | 29 | 2022 |
| Saliency methods for explaining adversarial attacks J Gu, V Tresp Workshop on Human-Centric Machine Learning, NeurIPS 2019, 2019 | 28 | 2019 |
| Interpretable graph capsule networks for object recognition J Gu Proceedings of the AAAI Conference on Artificial Intelligence 35 (2), 1469-1477, 2021 | 27 | 2021 |
| Effective and Efficient Vote Attack on Capsule Networks J Gu, B Wu, V Tresp International Conference on Learning Representations (ICLR), 2021, 2021 | 26 | 2021 |
| Capsule network is not more robust than convolutional network J Gu, V Tresp, H Hu IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 14309-14317, 2021 | 25 | 2021 |
| Towards efficient adversarial training on vision transformers B Wu*, J Gu*, Z Li, D Cai, X He, W Liu European Conference on Computer Vision (ECCV), 307-325, 2022 | 23 | 2022 |
| Understanding bias in machine learning J Gu, D Oelke Workshop on Visualization for AI Explainability, IEEE Vis 2018, 2019 | 23 | 2019 |
| Attacking Adversarial Attacks as A Defense B Wu, H Pan, L Shen, J Gu, S Zhao, Z Li, D Cai, X He, W Liu arXiv preprint arXiv:2106.04938, 2021 | 21 | 2021 |
| Search for better students to learn distilled knowledge J Gu, V Tresp European Conference on Artificial Intelligence (ECAI), 1159-1165, 2020 | 20 | 2020 |
| Semantics for global and local interpretation of deep neural networks J Gu, V Tresp arXiv preprint arXiv:1910.09085, 2019 | 15 | 2019 |
| Adversarial examples on segmentation models can be easy to transfer J Gu, H Zhao, V Tresp, P Torr arXiv preprint arXiv:2111.11368, 2021 | 11 | 2021 |
| Contextual prediction difference analysis for explaining individual image classifications J Gu, V Tresp arXiv preprint arXiv:1910.09086, 2019 | 10 | 2019 |
| A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models J Gu, Z Han, S Chen, A Beirami, B He, G Zhang, R Liao, Y Qin, V Tresp, ... arXiv preprint arXiv:2307.12980, 2023 | 9 | 2023 |
| Watermark vaccine: Adversarial attacks to prevent watermark removal X Liu, J Liu, Y Bai, J Gu, T Chen, X Jia, X Cao European Conference on Computer Vision (ECCV), 1-17, 2022 | 9 | 2022 |
| ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations Z Han, R Liao, J Gu, Y Zhang, Z Ding, Y Gu, H Köppl, H Schütze, V Tresp Findings of the Association for Computational Linguistics: ACL 2023, 2022 | 6* | 2022 |
| Neural network memorization dissection J Gu, V Tresp Workshop on Machine Learning with Guarantees, NeurIPS 2019, 2019 | 6 | 2019 |
| Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models S Chen*, J Gu*, Z Han, Y Ma, P Torr, V Tresp arXiv preprint arXiv:2306.02080, 2023 | 5 | 2023 |
Volker TrespLudwig Maximilian University of Munich / München (LMU)Verified email at dbs.ifi.lmu.de
