arXiv (All)
open access
Sparse Fine-Tuning for Parameter-Efficient Adversarial Training
Deep neural networks achieve strong performance across many tasks but remain vulnerable to adversarial perturbations. Adversarial training (AT) is one of the most effective defenses, yet it suffers from high memory cost on large models and the persistent clean-robust accuracy trade-off. Recent work
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion#1342601
- Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion#1318905
- Comprehensive Review on Advanced Adversarial Attack and Defense Strategies in Deep Neural Network#714011
- Comprehensive Review on Advanced Adversarial Attack and Defense Strategies in Deep Neural Network#85092
- UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?#1132277
Record · ID 1490850
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive