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Few Shot Image Classification On Cifar Fs 5 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Paper Title
CAML [Laion-2b]93.5Context-Aware Meta-Learning
PT+MAP+SF+SOT (transductive)92.83The Self-Optimal-Transport Feature Transform
PT+MAP+SF+BPA (transductive)92.83The Balanced-Pairwise-Affinities Feature Transform
P>M>F (P=DINO-ViT-base, M=ProtoNet)92.2Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
PEMnE-BMS*91.86Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
Illumination Augmentation91.09Sill-Net: Feature Augmentation with Separated Illumination Representation
LST+MAP90.73Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network
PT+MAP90.68Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
BAVARDAGE90.63Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification
HCTransformers90.50Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning
EASY 3xResNet12 (transductive)90.47EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
EASY 2xResNet12 1/√2 (transductive)90.2EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
Invariance-Equivariance89.74Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning
ACC + Amphibian89.3Generalized Adaptation for Few-Shot Learning
pseudo-shots89.12Extended Few-Shot Learning: Exploiting Existing Resources for Novel Tasks
EASY 3xResNet12 (inductive)89.0EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
FewTURE88.90Rethinking Generalization in Few-Shot Classification
SKD88.9Self-supervised Knowledge Distillation for Few-shot Learning
MetaQDA88.79Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
MetaOptNet-SVM+Task Aug88.38Task Augmentation by Rotating for Meta-Learning
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Few Shot Image Classification On Cifar Fs 5 1 | SOTA | HyperAI