SOTAVerified

Few-Shot Learning

Few-Shot Learning is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to the Few-Shot Learning problem is to learn a common representation for various tasks and train task specific classifiers on top of this representation.

Source: Penalty Method for Inversion-Free Deep Bilevel Optimization

Papers

Showing 651700 of 2964 papers

TitleStatusHype
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
Negative Margin Matters: Understanding Margin in Few-shot ClassificationCode1
Instance Credibility Inference for Few-Shot LearningCode1
Learning What to Learn for Video Object SegmentationCode1
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?Code1
Selecting Relevant Features from a Multi-domain Representation for Few-shot ClassificationCode1
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot LearningCode1
Domain-Adaptive Few-Shot LearningCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classificationCode1
Meta-Learning Initializations for Low-Resource Drug DiscoveryCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
On the Texture Bias for Few-Shot CNN SegmentationCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
Meta-Learned Confidence for Few-shot LearningCode1
Few-shot Natural Language Generation for Task-Oriented DialogCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot LearningCode1
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning NetworkCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Meta-Learning of Neural Architectures for Few-Shot LearningCode1
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot LearningCode1
Penalty Method for Inversion-Free Deep Bilevel OptimizationCode1
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
Artistic Glyph Image Synthesis via One-Stage Few-Shot LearningCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Meta-Transfer Learning through Hard TasksCode1
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAMLCode1
Meta-Learning with Implicit GradientsCode1
Charting the Right Manifold: Manifold Mixup for Few-shot LearningCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
Large-Scale Long-Tailed Recognition in an Open WorldCode1
A Closer Look at Few-shot ClassificationCode1
Meta-Learning with Differentiable Convex OptimizationCode1
Hyperbolic Image EmbeddingsCode1
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric ImagesCode1
BioBERT: a pre-trained biomedical language representation model for biomedical text miningCode1
Human few-shot learning of compositional instructionsCode1
Few-Shot Learning via Embedding Adaptation with Set-to-Set FunctionsCode1
Meta-Transfer Learning for Few-Shot LearningCode1
Few-shot Object Detection via Feature ReweightingCode1
One-Shot Instance SegmentationCode1
How to train your MAMLCode1
Meta-Learning with Latent Embedding OptimizationCode1
Meta-learning with differentiable closed-form solversCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
On First-Order Meta-Learning AlgorithmsCode1
Learning to Compare: Relation Network for Few-Shot LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91Unverified
2gpt-4-0125-previewAccuracy52.49Unverified
3gpt-3.5-turboAccuracy41.48Unverified
4gpt-3.5-turboAccuracy37.06Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63Unverified
6yikuan8/Clinical-LongformerAccuracy25.55Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06Unverified
8yikuan8/Clinical-LongformerAccuracy25.04Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27Unverified
2SaSPA + CAL4-shot Accuracy48.3Unverified
3Real-Guidance + CAL4-shot Accuracy41.5Unverified
4CAL4-shot Accuracy40.9Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2Unverified
2CALHarmonic mean35.2Unverified
3Variational Prompt TuningHarmonic mean34.69Unverified
4Real-Guidance + CALHarmonic mean34.5Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7Unverified
2TIM-GDAccuracy87.4Unverified
3UNEM-GaussianAccuracy66.4Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75Unverified
2HCTransformers5 way 1~2 shot74.74Unverified
3HyperShotAccuracy53.18Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7Unverified
2Real-Guidance + CAL4-shot Accuracy44.3Unverified
3CAL4-shot Accuracy42.2Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74Unverified
2DPGNAcc67.6Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79Unverified