| PT+MAP+SF+SOT (transductive) |
- Accuracy
- 95.80
|
The Self-Optimal-Transport Feature Transform 2022-04-06 |
코드 1 |
| CAML [Laion-2b] |
- Accuracy
- 95.8
|
Context-Aware Meta-Learning 2023-10-17 |
코드 1 |
| PT+MAP+SF+BPA (transductive) |
- Accuracy
- 95.80
|
The Balanced-Pairwise-Affinities Feature Transform 2024-06-25 |
코드 1 |
| PT+MAP+SF (transductive) |
- Accuracy
- 95.48
|
Few-Shot Learning by Integrating Spatial and Frequency Representation 2021-05-11 |
코드 1 |
| PEMnE-BMS* |
- Accuracy
- 94.78
|
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning 2021-10-18 |
코드 1 |
| Illumination Augmentation |
- Accuracy
- 94.73
|
Sill-Net: Feature Augmentation with Separated Illumination Representation 2021-02-06 |
코드 1 |
| LST+MAP |
- Accuracy
- 91.68
|
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network 2021-02-09 |
코드 1 |
| PT+MAP |
- Accuracy
- 91.55%
|
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning 2020-06-06 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 |
| EASY 3xResNet12 (transductive) |
- Accuracy
- 90.56
|
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients 2022-01-24 |
코드 1 · 코드 2 · 코드 3 |
| EASY 4xResNet12 (transductive) |
- Accuracy
- 90.5
|
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients 2022-01-24 |
코드 1 · 코드 2 · 코드 3 |
| BAVARDAGE |
- Accuracy
- 90.42
|
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification 2022-09-18 |
연결 없음 |
| ICI |
- Accuracy
- 89.58
|
Instance Credibility Inference for Few-Shot Learning 2020-03-26 |
코드 1 |
| Transfer+SGC |
- Accuracy
- 88.35%
|
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification 2020-01-27 |
코드 1 |
| ESPT |
- Accuracy
- 85.45
|
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning 2023-04-26 |
코드 1 |
| TDM |
- Accuracy
- 84.36
|
Task Discrepancy Maximization for Fine-grained Few-Shot Classification 2022-07-04 |
코드 1 |
| BD-CSPN + ESFR (ResNet-18) |
- Accuracy
- 82.68
|
Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification 2021-06-22 |
코드 1 |
| TIM-GD |
- Accuracy
- 82.2%
|
Transductive Information Maximization For Few-Shot Learning 2020-08-25 |
코드 1 · 코드 2 |
| LaplacianShot |
- Accuracy
- 80.96
|
Laplacian Regularized Few-Shot Learning 2020-06-29 |
코드 1 · 코드 2 |
| S2M2R |
- Accuracy
- 80.68
|
Charting the Right Manifold: Manifold Mixup for Few-shot Learning 2019-07-28 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 · 코드 7 · 코드 8 |
| RENet |
- Accuracy
- 79.49
|
Relational Embedding for Few-Shot Classification 2021-08-22 |
코드 1 |
| Ours |
- Accuracy
- 79.12
|
Variational Feature Disentangling for Fine-Grained Few-Shot Classification 2020-10-07 |
연결 없음 |
| EASY 3xResNet12 (inductive) |
- Accuracy
- 78.56
|
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients 2022-01-24 |
코드 1 · 코드 2 · 코드 3 |
| EASY 4xResNet12 (inductive) |
- Accuracy
- 77.97
|
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients 2022-01-24 |
코드 1 · 코드 2 · 코드 3 |
| AmdimNet |
- Accuracy
- 77.09
|
Self-Supervised Learning For Few-Shot Image Classification 2019-11-14 |
코드 1 · 코드 2 |
| MergedNet-Max |
- Accuracy
- 75.34
|
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layers 2022-10-14 |
코드 1 |
| Neg-Margin |
- Accuracy
- 72.66
|
Negative Margin Matters: Understanding Margin in Few-shot Classification 2020-03-26 |
코드 1 |
| DKT + BNCosSim |
- Accuracy
- 72.27
|
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels 2019-10-11 |
코드 1 · 코드 2 · 코드 3 |
| Self-Critique and Adapt + High-End MAML++ |
- Accuracy
- 70.46
|
Learning to learn via Self-Critique 2019-05-24 |
코드 1 |
| Delta-encoder |
- Accuracy
- 69.8
|
Delta-encoder: an effective sample synthesis method for few-shot object recognition 2018-06-12 |
코드 1 |
| feat (ProtoNet) |
- Accuracy
- 68.65
|
Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions 2018-12-10 |
코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5 · 코드 6 |