arXiv (OAI Expanded)
open access
Noise-Aware and Dynamically Adaptive Federated Defense Framework for SAR Image Target Recognition
As a critical application of computational intelligence in remote sensing, deep learning-based synthetic aperture radar (SAR) image target recognition facilitates intelligent perception but typically relies on centralized training, where multi-source SAR data are uploaded to a single server, raising
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
- Improve Trust and Adversarial Robustness of AI models in Remote Sensing#307622
- Multi-View Mixture-of-Experts with Vision-Language Reranking for Cross-View Object Geo-Localization#1011835
- GAIDS: A Generative AI-Driven Defense Symbiosis Framework for Intelligent Transformation of the Defense Industry#694465
- NeuroTrustNet: a cost-effective multimodal ensemble framework for brain tumor classification under cross-dataset variability.#1097825
- ProSR: Semantic-Prototype-Guided Discrete Modeling for Physically Consistent SAR Super-Resolution#924263
Record · ID 1084107
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