Research map / Adversarial robustness and security / Certified and empirical robustness
Your Classifier Can Do More: Towards Balancing the Gaps in Classification, Robustness, and Generation
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
- Towards Bridging the Performance Gaps of Joint Energy-Based Models (CVPR 2023 · similarity 0.94)
- Explaining the role of Intrinsic Dimensionality in Adversarial Training (ICML 2025 · similarity 0.85)
- Learning normalized image densities via dual score matching (NeurIPS 2025 · similarity 0.85)
- Improving Adversarial Energy-Based Model via Diffusion Process (ICML 2024 · similarity 0.85)
- Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization (NeurIPS 2025 · similarity 0.85)
- Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood (ICLR 2024 · similarity 0.84)
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.