Few Shot Image Classification On Fc100 5 Way 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Paper Title	Repository
ACC + Amphibian	66.9	Generalized Adaptation for Few-Shot Learning	-
Multi-Task Learning	57.7	Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
MTL	57.6	Meta-Transfer Learning for Few-Shot Learning
Invariance-Equivariance	65.3	Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
SSFormers	58.92	Sparse Spatial Transformers for Few-Shot Learning
TADAM	56.1	TADAM: Task dependent adaptive metric for improved few-shot learning
EASY 2xResNet12 1/√2 (transductive)	65.82	EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
BAVARDAGE	70.60	Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification	-
SKD	63.1	Self-supervised Knowledge Distillation for Few-shot Learning
EASY 3xResNet12 (inductive)	64.74	EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
MetaOptNet-SVM+Task Aug	67.17	Task Augmentation by Rotating for Meta-Learning
HCTransformers	66.42	Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
MetaOptNet-SVM-trainval	62.5	Meta-Learning with Differentiable Convex Optimization
ConstellationNets	59.7	Constellation Nets for Few-Shot Learning
FewTURE	63.81	Rethinking Generalization in Few-Shot Classification
MSENet	66.27	Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms
pseudo-shots	61.58	Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
EASY 2xResNet12 1/√2 (inductive)	64.14	EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
R2-D2+Task Aug	67.66	Task Augmentation by Rotating for Meta-Learning
MCRNet-SVM	57.8	Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach

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