Research map / Label-efficient and robust learning / Self-supervised and contrastive
Adversarially Robust Representations with Smooth Encoders
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
- Adversarial Robustness of Supervised Sparse Coding (NeurIPS 2020 · similarity 0.90)
- Improving VAEs' Robustness to Adversarial Attack (ICLR 2021 · similarity 0.89)
- Understanding Robust Learning through the Lens of Representation Similarities (NeurIPS 2022 · similarity 0.89)
- LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders (NeurIPS 2025 · similarity 0.88)
- Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off (ICLR 2025 · similarity 0.87)
- Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization (NeurIPS 2025 · 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.
