Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning

Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data

NeurIPS 2023 · Label-efficient and robust learning › Zero-/few-shot and meta-learning · approach: Fine-tuning strategies for few-shot · 1 citations (OpenAlex)

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

• Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization (NeurIPS 2022 · similarity 0.89)
• Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (ICLR 2020 · similarity 0.87)
• Pushing the Limit of Fine-Tuning for Few-Shot Learning: Where Feature Reusing Meets Cross-Scale Attention (AAAI 2024 · similarity 0.86)
• Scaling Few-Shot Learning for the Open World (AAAI 2024 · similarity 0.86)
• Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning (CVPR 2024 · similarity 0.86)
• Provably Improving Generalization of Few-shot models with Synthetic Data (ICML 2025 · similarity 0.86)