arXiv (All)
open access
Can One-Shot Test-Time Data Augmentation Help with Generalization?
Data augmentation is crucial for model generalization, but existing methods are mostly centered on the training stage. Test-time augmentation, while underexplored, can be practically effective for generalization while avoiding extra model parameters or fine-tuning. Given the increasing training cost
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
- STAMP: Predicting Out-of-Distribution Generalization without Target Data#1117003
- SPeaR: Test-Time Adaptation with Steering Primitives for Realigning Representations#1050297
- Demystifying Training-Time Augmentation for Data-Constrained Language Model Pretraining#635875
- Demystifying Training-Time Augmentation for Data-Constrained Language Model Pretraining#633526
- The Piggyback Hypothesis of Generalization: Explaining and Mitigating Emergent Misalignment#1156380
Record · ID 973052
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