| Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts | ✓ Link | 85.27 | SMAT (DINO-VIT-Base-16-224) | 2024-03-13 |
| Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference | ✓ Link | 84.75 | P>M>F (P=DINO-ViT-base, M=ProtoNet) | 2022-04-15 |
| Task-Specific Preconditioner for Cross-Domain Few-Shot Learning | | 81.40 | TSP (ResNet18; applied on TA^2-Net) | 2024-12-20 |
| Cross-domain Few-shot Learning with Task-specific Adapters | ✓ Link | 78.07 | TSA (ResNet18, URL, residual adapters, 84x84 image, shuffled data, scratch, MDL) | 2021-07-01 |
| Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification | ✓ Link | 76.1 | UpperCaSE-EfficientNetB0 | 2022-06-20 |
| Universal Representation Learning from Multiple Domains for Few-shot Classification | ✓ Link | 75.75 | URL (ResNet18, 84x84 image, shuffled data, scratch, MDL) | 2021-03-25 |
| Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification | ✓ Link | 74.9 | UpperCaSE-ResNet50 | 2022-06-20 |
| Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition | ✓ Link | 74.3 | URT+MQDA | 2021-01-08 |
| A Universal Representation Transformer Layer for Few-Shot Image Classification | ✓ Link | 72.15 | URT | 2020-06-21 |
| Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification | ✓ Link | 70.72 | SUR | 2020-03-20 |
| Enhancing Few-Shot Image Classification with Unlabelled Examples | ✓ Link | 70.32 | Transductive CNAPS | 2020-06-17 |
| Improved Few-Shot Visual Classification | ✓ Link | 69.86 | Simple CNAPS | 2019-12-07 |
| Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification | ✓ Link | 69.3 | SUR-pnf | 2020-03-20 |
| Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning | ✓ Link | 68.89 | Invariance-Equivariance | 2021-03-01 |
| Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes | ✓ Link | 66.9 | CNAPs | 2019-06-18 |
| Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples | ✓ Link | 63.428 | fo-Proto-MAML | 2019-03-07 |
| Prototypical Networks for Few-shot Learning | ✓ Link | 60.573 | Prototypical Networks | 2017-03-15 |
| Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples | ✓ Link | 58.758 | Finetune | 2019-03-07 |
| Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | ✓ Link | 57.024 | fo-MAML | 2017-03-09 |
| Matching Networks for One Shot Learning | ✓ Link | 56.247 | Matching Networks | 2016-06-13 |
| Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples | ✓ Link | 54.319 | k-NN | 2019-03-07 |
| Learning to Compare: Relation Network for Few-Shot Learning | ✓ Link | 53.315 | Relation Networks | 2017-11-16 |