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 24012450 of 2964 papers

TitleStatusHype
Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English LanguageCode0
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot LearningCode0
Class-Agnostic CountingCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and ClassificationCode0
Prototypical Calibration for Few-shot Learning of Language ModelsCode0
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registrationCode0
Dynamic Semantic Matching and Aggregation Network for Few-shot Intent DetectionCode0
Learning Prototype Representations Across Few-Shot Tasks for Event DetectionCode0
LGM-Net: Learning to Generate Matching Networks for Few-Shot LearningCode0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
Learning New Tasks from a Few Examples with Soft-Label PrototypesCode0
SSM-Net for Plants Disease Identification in Low Data RegimeCode0
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningCode0
Dual-level Mixup for Graph Few-shot Learning with Fewer TasksCode0
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable PromptingCode0
Towards Offensive Language Identification for Dravidian LanguagesCode0
StablePT: Towards Stable Prompting for Few-shot Learning via Input SeparationCode0
Limited Data Rolling Bearing Fault Diagnosis with Few-shot LearningCode0
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse TasksCode0
ProtSi: Prototypical Siamese Network with Data Augmentation for Few-Shot Subjective Answer EvaluationCode0
Unlocking the Full Potential of Small Data with Diverse SupervisionCode0
Proxy Network for Few Shot LearningCode0
Learning from the Tangram to Solve Mini Visual TasksCode0
Towards Context-Agnostic Learning Using Synthetic DataCode0
Learning Deep Disentangled Embeddings with the F-Statistic LossCode0
Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot LearningCode0
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production CodeCode0
Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad PredictionCode0
LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine FeedbackCode0
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language ModelsCode0
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot LearningCode0
learn2learn: A Library for Meta-Learning ResearchCode0
Do Tutors Learn from Equity Training and Can Generative AI Assess It?Code0
StarNet: towards Weakly Supervised Few-Shot Object DetectionCode0
VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability EstimationCode0
Improving Few-Shot Learning through Multi-task Representation Learning TheoryCode0
Do Prompt-Based Models Really Understand the Meaning of their Prompts?Code0
Does MAML Only Work via Feature Re-use? A Data Centric PerspectiveCode0
Logarithm-transform aided Gaussian Sampling for Few-Shot LearningCode0
LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio TaggingCode0
Towards Sample-efficient Overparameterized Meta-learningCode0
Active Few-Shot Learning with FASLCode0
LCNN: Lookup-based Convolutional Neural NetworkCode0
An Ensemble of Epoch-wise Empirical Bayes for Few-shot LearningCode0
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot LearningCode0
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language UnderstandingCode0
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
LAVA: Label-efficient Visual Learning and AdaptationCode0
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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