Research map / Adversarial robustness and security / Certified and empirical robustness
Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination 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
- Your Classifier Can Do More: Towards Balancing the Gaps in Classification, Robustness, and Generation (CVPR 2026 · similarity 0.93)
- Towards Bridging the Performance Gaps of Joint Energy-Based Models (CVPR 2023 · similarity 0.91)
- A Unified Contrastive Energy-based Model for Understanding the Generative Ability of Adversarial Training (ICLR 2022 · similarity 0.91)
- Improving Adversarial Energy-Based Model via Diffusion Process (ICML 2024 · similarity 0.89)
- Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood (ICLR 2024 · similarity 0.89)
- Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination (NeurIPS 2024 · similarity 0.87)
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.
