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홈
SOTA
소수 샷 이미지 분류
Few Shot Image Classification On Cifar Fs 5 1
Few Shot Image Classification On Cifar Fs 5 1
평가 지표
Accuracy
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
Paper Title
Repository
CAML [Laion-2b]
93.5
Context-Aware Meta-Learning
PT+MAP+SF+SOT (transductive)
92.83
The Self-Optimal-Transport Feature Transform
PT+MAP+SF+BPA (transductive)
92.83
The Balanced-Pairwise-Affinities Feature Transform
P>M>F (P=DINO-ViT-base, M=ProtoNet)
92.2
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
PEMnE-BMS*
91.86
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
Illumination Augmentation
91.09
Sill-Net: Feature Augmentation with Separated Illumination Representation
LST+MAP
90.73
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network
PT+MAP
90.68
Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
BAVARDAGE
90.63
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
-
HCTransformers
90.50
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
EASY 3xResNet12 (transductive)
90.47
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
EASY 2xResNet12 1/√2 (transductive)
90.2
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Invariance-Equivariance
89.74
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
ACC + Amphibian
89.3
Generalized Adaptation for Few-Shot Learning
-
pseudo-shots
89.12
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
EASY 3xResNet12 (inductive)
89.0
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
FewTURE
88.90
Rethinking Generalization in Few-Shot Classification
SKD
88.9
Self-supervised Knowledge Distillation for Few-shot Learning
MetaQDA
88.79
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
MetaOptNet-SVM+Task Aug
88.38
Task Augmentation by Rotating for Meta-Learning
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