Few Shot Image Classification On Tiered 1

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Paper Title	Repository
SKD	86.66	Self-supervised Knowledge Distillation for Few-shot Learning
Invariance-Equivariance	87.08	Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
HyperTransformer	73.9%	HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
MetaFun-Kernel	83.28	MetaFun: Meta-Learning with Iterative Functional Updates
Transductive CNAPS	81.8	Enhancing Few-Shot Image Classification with Unlabelled Examples
BD-CSPN + ESFR (WRN)	87.61	Unsupervised Embedding Adaptation via Early-Stage Feature Reconstruction for Few-Shot Classification
EASY 3xResNet12 (inductive)	88.33	EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
EASY 3xResNet12 (transductive)	89.76	EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
ICI	89.00	Instance Credibility Inference for Few-Shot Learning
GML (ResNet-12)	84.04	Geometric Mean Improves Loss For Few-Shot Learning	-
CAML [Laion-2b]	98.8	Context-Aware Meta-Learning	-
AM3-TADAM	82.58	Adaptive Cross-Modal Few-Shot Learning
pseudo-shots	86.82	Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
TIM-GD	89.8	Transductive Information Maximization For Few-Shot Learning
MTUNet+ResNet-18	77.82	Match Them Up: Visually Explainable Few-shot Image Classification
BAVARDAGE	90.41	Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification	-
LST	85.2	Learning to Self-Train for Semi-Supervised Few-Shot Classification
UniSiam	86.51	Self-Supervision Can Be a Good Few-Shot Learner
MetaQDA	89.56	Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
DiffKendall (Meta-Baseline, ResNet-12)	85.31	DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation

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