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Few Shot Image Classification On Cifar Fs 5

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
MCRNet-RR73.8Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
MetaOptNet-SVM+Task Aug76.75Task Augmentation by Rotating for Meta-Learning
Illumination Augmentation87.73Sill-Net: Feature Augmentation with Separated Illumination Representation
PT+MAP+SF+SOT (transductive)89.94 The Self-Optimal-Transport Feature Transform
MetaQDA75.83Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
BAVARDAGE87.35Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification-
MTUNet+ResNet-1866.31Match Them Up: Visually Explainable Few-shot Image Classification
SIB80.0Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
ConstellationNets75.4Constellation Nets for Few-Shot Learning
ACC + Amphibian73.1Generalized Adaptation for Few-Shot Learning-
Invariance-Equivariance77.87Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
RENet74.51Relational Embedding for Few-Shot Classification
ICI76.51Instance Credibility Inference for Few-Shot Learning
GML (ResNet-12)71.09Geometric Mean Improves Loss For Few-Shot Learning-
EASY 2xResNet12 1/√2 (transductive)86.99EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
EASY 3xResNet12 (transductive)87.16EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
pseudo-shots81.87Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
Adaptive Subspace Network78Adaptive Subspaces for Few-Shot Learning
EASY 2xResNet12 1/√2 (inductive)75.24EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
PT+MAP+SF+BPA (transductive)89.94The Balanced-Pairwise-Affinities Feature Transform
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