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홈
SOTA
소수 샷 이미지 분류
Few Shot Image Classification On Cifar Fs 5
Few Shot Image Classification On Cifar Fs 5
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Paper Title
Repository
PT+MAP+SF+SOT (transductive)
89.94
The Self-Optimal-Transport Feature Transform
PT+MAP+SF+BPA (transductive)
89.94
The Balanced-Pairwise-Affinities Feature Transform
PEMnE-BMS*
88.44
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
LST+MAP
87.79
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network
Illumination Augmentation
87.73
Sill-Net: Feature Augmentation with Separated Illumination Representation
PT+MAP
87.69
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
BAVARDAGE
87.35
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
-
EASY 3xResNet12 (transductive)
87.16
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
EASY 2xResNet12 1/√2 (transductive)
86.99
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
P>M>F (P=DINO-ViT-base, M=ProtoNet)
84.3
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
CAML [Laion-2b]
83.3
Context-Aware Meta-Learning
pseudo-shots
81.87
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
SIB
80.0
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
HCTransformers
78.89
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
Adaptive Subspace Network
78
Adaptive Subspaces for Few-Shot Learning
-
Invariance-Equivariance
77.87
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
FewTURE
77.76
Rethinking Generalization in Few-Shot Classification
R2-D2+Task Aug
77.66
Task Augmentation by Rotating for Meta-Learning
SKD
76.9
Self-supervised Knowledge Distillation for Few-shot Learning
MetaOptNet-SVM+Task Aug
76.75
Task Augmentation by Rotating for Meta-Learning
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