arXiv (All)
open access
Catastrophic Forgetting in Sequential Thermal Anti-UAV Detection: The Role of Scale-Conditioned Gradient Imbalance
Counter-UAV systems based on thermal infrared detection must stay accurate as operational datasets evolve, yet sequential fine-tuning causes catastrophic forgetting of prior tasks, a problem that remains insufficiently characterized in this domain. This continual-learning study measures the stabilit
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
- PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks#1155203
- Catastrophic Forgetting is Low-Rank: A Function-Space Theory for Continual Adaptation#282797
- XAI ‐Guided Continual Learning: Rationale, Methods, and Future Directions#1358684
- Homeostatic Continual Learning#1083272
- Formalizing Forgetting: Theory of Forgetting for Online Continual Learning#1325683
Record · ID 1218050
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