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홈
SOTA
소수 샷 이미지 분류
Few Shot Image Classification On Cub 200 5
Few Shot Image Classification On Cub 200 5
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Paper Title
Repository
CAML [Laion-2b]
98.7
Context-Aware Meta-Learning
PT+MAP+SF+BPA (transductive)
97.12
The Balanced-Pairwise-Affinities Feature Transform
PT+MAP+SF+SOT (transductive)
97.12
The Self-Optimal-Transport Feature Transform
PEMnE-BMS*
96.43
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
Illumination Augmentation
96.28
Sill-Net: Feature Augmentation with Separated Illumination Representation
LST+MAP
94.09
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network
ESPT
94.02
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning
PT+MAP
93.99
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
EASY 4xResNet12 (transductive)
93.5
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
BAVARDAGE
93.50
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
-
TDM
93.37
Task Discrepancy Maximization for Fine-grained Few-Shot Classification
ICI
92.48
Instance Credibility Inference for Few-Shot Learning
Transfer+SGC
92.14
Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification
EASY 3xResNet12 (inductive)
91.93
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
EASY 4xResNet12 (inductive)
91.59
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
VFD
91.48
Variational Feature Disentangling for Fine-Grained Few-Shot Classification
-
RENet
91.11
Relational Embedding for Few-Shot Classification
S2M2R
90.85
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
TIM-GD
90.8
Transductive Information Maximization For Few-Shot Learning
Neg-Margin
89.40
Negative Margin Matters: Understanding Margin in Few-shot Classification
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