HyperAI초신경
홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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Few Shot Image Classification On Fc100 5 Way 1

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Accuracy
Paper TitleRepository
ACC + Amphibian66.9Generalized Adaptation for Few-Shot Learning-
Multi-Task Learning57.7Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation
MTL57.6Meta-Transfer Learning for Few-Shot Learning
Invariance-Equivariance65.3Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
SSFormers58.92Sparse Spatial Transformers for Few-Shot Learning
TADAM56.1TADAM: Task dependent adaptive metric for improved few-shot learning
EASY 2xResNet12 1/√2 (transductive)65.82EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
BAVARDAGE70.60Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification-
SKD63.1Self-supervised Knowledge Distillation for Few-shot Learning
EASY 3xResNet12 (inductive)64.74EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
MetaOptNet-SVM+Task Aug67.17Task Augmentation by Rotating for Meta-Learning
HCTransformers66.42Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
MetaOptNet-SVM-trainval62.5Meta-Learning with Differentiable Convex Optimization
ConstellationNets59.7Constellation Nets for Few-Shot Learning
FewTURE63.81Rethinking Generalization in Few-Shot Classification
MSENet66.27Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention Mechanisms
pseudo-shots61.58Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
EASY 2xResNet12 1/√2 (inductive)64.14EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
R2-D2+Task Aug67.66Task Augmentation by Rotating for Meta-Learning
MCRNet-SVM57.8Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
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