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 451500 of 2964 papers

TitleStatusHype
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language ModelsCode1
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot LearningCode1
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
MetaAudio: A Few-Shot Audio Classification BenchmarkCode1
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequencesCode1
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical NotesCode1
MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-TuningCode1
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingCode1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPCode1
Meta-Learning in Neural Networks: A SurveyCode1
Meta-Learning Siamese Network for Few-Shot Text ClassificationCode1
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot LearningCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Meta-Learning with Implicit GradientsCode1
Meta-Learning with Task-Adaptive Loss Function for Few-Shot LearningCode1
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
CLUES: Few-Shot Learning Evaluation in Natural Language UnderstandingCode1
Artistic Glyph Image Synthesis via One-Stage Few-Shot LearningCode1
Meta-SGD: Learning to Learn Quickly for Few-Shot LearningCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
AdapterHub Playground: Simple and Flexible Few-Shot Learning with AdaptersCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence EmbeddingsCode1
Model-Agnostic Few-Shot Open-Set RecognitionCode1
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksCode1
BOIL: Towards Representation Change for Few-shot LearningCode1
MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMsCode1
A Simple Exponential Family Framework for Zero-Shot LearningCode1
D^2ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action RecognitionCode1
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
CodeIE: Large Code Generation Models are Better Few-Shot Information ExtractorsCode1
Code Summarization Beyond Function LevelCode1
Multi-view Contrastive Learning for Online Knowledge DistillationCode1
AskIt: Unified Programming Interface for Programming with Large Language ModelsCode1
Mutual-Information Based Few-Shot ClassificationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Domain-Adaptive Few-Shot LearningCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
Neural Fine-Tuning Search for Few-Shot LearningCode1
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsCode1
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many ClassesCode1
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learningCode1
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
A Closer Look at Few-shot ClassificationCode1
Data Distributional Properties Drive Emergent In-Context Learning in TransformersCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter ViewCode1
On Episodes, Prototypical Networks, and Few-shot LearningCode1
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language InferenceCode1
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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