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 12011250 of 2964 papers

TitleStatusHype
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency ParsingCode0
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive SummarizationCode0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Improved transferability of self-supervised learning models through batch normalization finetuningCode0
Improved Visually Prompted Keyword Localisation in Real Low-Resource SettingsCode0
Continual Few-Shot Learning for Text ClassificationCode0
Few-shot learning via tensor hallucinationCode0
A Closer Look at the Training Strategy for Modern Meta-LearningCode0
Continual Adversarial DefenseCode0
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language TasksCode0
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement LearningCode0
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained EnvironmentsCode0
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer ModelsCode0
FLEURS: Few-shot Learning Evaluation of Universal Representations of SpeechCode0
A Task-aware Dual Similarity Network for Fine-grained Few-shot LearningCode0
Few-shot learning through contextual data augmentationCode0
Contextualizing Enhances Gradient Based Meta LearningCode0
HyperPlanes: Hypernetwork Approach to Rapid NeRF AdaptationCode0
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot LearningCode0
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine TranslationCode0
Contextual Gradient Scaling for Few-Shot LearningCode0
HQP: A Human-Annotated Dataset for Detecting Online PropagandaCode0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
Hierarchy-based Image Embeddings for Semantic Image RetrievalCode0
Hierarchical Variational Memory for Few-shot Learning Across DomainsCode0
High-order structure preserving graph neural network for few-shot learningCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNsCode0
Hidden Entity Detection from GitHub Leveraging Large Language ModelsCode0
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive StudyCode0
Adaptive Posterior Learning: few-shot learning with a surprise-based memory moduleCode0
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property PredictionCode0
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property PredictionCode0
Grid Cell Path Integration For Movement-Based Visual Object RecognitionCode0
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation ExtractionCode0
Graph Few-shot Learning via Knowledge TransferCode0
Graph Few-shot Learning with Task-specific StructuresCode0
GSHOT: Few-shot Generative Modeling of Labeled GraphsCode0
Few-shot Learning for Named Entity Recognition in Medical TextCode0
Few-shot Learning for Multi-modal Social Media Event FilteringCode0
GPS: Genetic Prompt Search for Efficient Few-shot LearningCode0
AI-Assisted Colonoscopy: Polyp Detection and Segmentation using Foundation ModelsCode0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly DetectionCode0
Gotta Learn Fast: A New Benchmark for Generalization in RLCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Generative Transfer Learning: Covid-19 Classification with a few Chest X-ray ImagesCode0
Gestalt-Guided Image Understanding for Few-Shot LearningCode0
Global Convolutional Neural ProcessesCode0
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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