Research map / Adversarial robustness and security / Certified and empirical robustness
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Task-Robust Pre-Training for Worst-Case Downstream Adaptation (NeurIPS 2023 · similarity 0.92)
- Initialization Matters for Adversarial Transfer Learning (CVPR 2024 · similarity 0.91)
- Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning (CVPR 2020 · similarity 0.91)
- Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data (ICLR 2025 · similarity 0.89)
- Adversarially Robust Hypothesis Transfer Learning (ICML 2024 · similarity 0.88)
- Adversarially Robust Multi-task Representation Learning (NeurIPS 2024 · similarity 0.88)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.