Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- TaskNorm: Rethinking Batch Normalization for Meta-Learning (ICML 2020 · similarity 0.93)
- Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning (NeurIPS 2021 · similarity 0.92)
- Four Things Everyone Should Know to Improve Batch Normalization (ICLR 2020 · similarity 0.92)
- How Does Batch Normalization Help Optimization? (NeurIPS 2018 · similarity 0.91)
- Deconstructing the Regularization of BatchNorm (ICLR 2021 · similarity 0.91)
- Reducing Divergence in Batch Normalization for Domain Adaptation (AAAI 2025 · similarity 0.90)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.