共 33 条
- [1] Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 8482 - 8490
- [2] Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2019, : 2909 - 2915
- [3] Improving Adversarial Robustness via Attention and Adversarial Logit Pairing FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2022, 4
- [4] Improving Adversarial Robustness via Guided Complement Entropy 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, : 4880 - 4888
- [5] Improving Adversarial Robustness of Detector via Objectness Regularization PATTERN RECOGNITION AND COMPUTER VISION, PT IV, 2021, 13022 : 252 - 262
- [6] Improving Adversarial Robustness via Information Bottleneck Distillation ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
- [7] Improving Adversarial Robustness via Promoting Ensemble Diversity INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 97, 2019, 97
- [8] Improving Adversarial Robustness of CNNs via Maximum Margin APPLIED SCIENCES-BASEL, 2022, 12 (15):
- [9] Improving Adversarial Robustness via Mutual Information Estimation INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 2022,
- [10] Improving Adversarial Robustness via Distillation-Based Purification APPLIED SCIENCES-BASEL, 2023, 13 (20):