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SOTA
Few Shot Image Classification
Few Shot Image Classification On Cifar Fs 5
Few Shot Image Classification On Cifar Fs 5
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
Repository
MCRNet-RR
73.8
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
MetaOptNet-SVM+Task Aug
76.75
Task Augmentation by Rotating for Meta-Learning
Illumination Augmentation
87.73
Sill-Net: Feature Augmentation with Separated Illumination Representation
PT+MAP+SF+SOT (transductive)
89.94
The Self-Optimal-Transport Feature Transform
MetaQDA
75.83
Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
BAVARDAGE
87.35
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
-
MTUNet+ResNet-18
66.31
Match Them Up: Visually Explainable Few-shot Image Classification
SIB
80.0
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
ConstellationNets
75.4
Constellation Nets for Few-Shot Learning
ACC + Amphibian
73.1
Generalized Adaptation for Few-Shot Learning
-
Invariance-Equivariance
77.87
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
RENet
74.51
Relational Embedding for Few-Shot Classification
ICI
76.51
Instance Credibility Inference for Few-Shot Learning
GML (ResNet-12)
71.09
Geometric Mean Improves Loss For Few-Shot Learning
-
EASY 2xResNet12 1/√2 (transductive)
86.99
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
EASY 3xResNet12 (transductive)
87.16
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
pseudo-shots
81.87
Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
Adaptive Subspace Network
78
Adaptive Subspaces for Few-Shot Learning
EASY 2xResNet12 1/√2 (inductive)
75.24
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
PT+MAP+SF+BPA (transductive)
89.94
The Balanced-Pairwise-Affinities Feature Transform
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