Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
How Fine-Tuning Allows for Effective Meta-Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use task-specific representation fine-tuning during meta-training instead of a frozen-representation objective | AdaptRep vs. FrozenRep · Linear hard-case task family | target excess risk | Helped |
| Use task-specific representation fine-tuning during meta-training instead of a frozen-representation objective | AdaptRep vs. FrozenRep · Nonlinear ReLU hard-case task family | target loss | Helped |
| Use task-specific representation fine-tuning during meta-training instead of a frozen-representation objective | AdaptRep vs. FrozenRep · Linear hard-case simulation | worst-case target excess risk | Helped |
| Use task-specific representation fine-tuning during meta-training instead of a frozen-representation objective | AdaptRep vs. FrozenRep · Linear hard-case simulation | representation subspace alignment | Helped |
Closest papers by meaning
- The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning (NeurIPS 2020 · similarity 0.89)
- LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views (ICML 2024 · similarity 0.89)
- Provable Meta-Learning with Low-Rank Adaptations (NeurIPS 2025 · similarity 0.88)
- Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks (ICLR 2024 · similarity 0.88)
- Learning to Learn with Contrastive Meta-Objective (NeurIPS 2025 · similarity 0.87)
- Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models (ICML 2024 · similarity 0.87)
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