S-Space: Seoul National University
metadata only
FedLPA: Local Prior Alignment for Heterogeneous Federated Generalized Category Discovery
Federated Generalized Category Discovery (Fed-GCD) aims to train a global model that classifies seen classes while discovering novel ones from data distributed across heterogeneous clients. Existing GCD methods often rely on unrealistic assumptions, such as prior knowledge of the number of novel cla
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
- From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning#168271
- FedMIX-P: Mixing Local and Global Preconditioners for Federated Vision and Language Model Training#1156787
- Prototype-guided Bilateral Alignment Multimodal Federated Learning#1143526
- Where Decoder Cosine Similarity Fails for SAE Feature Flow Discovery#1070080
- ThaiTrees: Thai Syntactic Dependency Trees Across Domains#1064102
Record · ID 1428325
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