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 28012850 of 2964 papers

TitleStatusHype
Few-shot learning through contextual data augmentationCode0
Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and BeyondCode0
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language GenerationCode0
Meta Architecture SearchCode0
Contextual Gradient Scaling for Few-Shot LearningCode0
The Effect of Diversity in Meta-LearningCode0
Conditional Prototype Rectification Prompt LearningCode0
Conceptual Design Generation Using Large Language ModelsCode0
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog SystemsCode0
Adaptive Anchor Label Propagation for Transductive Few-Shot LearningCode0
Variational Memory Addressing in Generative ModelsCode0
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher EducationCode0
On the Multilingual Capabilities of Very Large-Scale English Language ModelsCode0
Semantic-Aware Graph Matching Mechanism for Multi-Label Image RecognitionCode0
Variational Metric Scaling for Metric-Based Meta-LearningCode0
Semantic Cross Attention for Few-shot LearningCode0
On the Role of Neural Collapse in Meta Learning Models for Few-shot LearningCode0
Semantic Projection Network for Zero- and Few-Label Semantic SegmentationCode0
ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)Code0
Semantics-driven Attentive Few-shot Learning over Clean and Noisy SamplesCode0
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure PredictionCode0
Few-shot Learning for Named Entity Recognition in Medical TextCode0
AutoProtoNet: Interpretability for Prototypical NetworksCode0
The representation landscape of few-shot learning and fine-tuning in large language modelsCode0
Automatic Generation of Fashion Images using Prompting in Generative Machine Learning ModelsCode0
On Unifying Misinformation DetectionCode0
EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier LogitsCode0
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot LearningCode0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of DomainsCode0
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline TimepointCode0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Few-shot Learning for Multi-modal Social Media Event FilteringCode0
Semi-Supervised Few-Shot Learning via Multi-Factor ClusteringCode0
The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 LanguagesCode0
Semi-Supervised Few-Shot Learning with Prototypical Random WalksCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture RecognitionCode0
Automated Few-shot Classification with Instruction-Finetuned Language ModelsCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain StudyCode0
Karyotype AI for Precision OncologyCode0
UNEM: UNrolled Generalized EM for Transductive Few-Shot LearningCode0
Few-Shot Learning for Argument Aspects of the Nuclear Energy DebateCode0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Few-Shot Learning by Explicit Physics Integration: An Application to Groundwater Heat TransportCode0
P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot ClassificationCode0
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked AutoencodersCode0
Sentence Simplification via Large Language ModelsCode0
Generalizing from a Few Examples: A Survey on Few-Shot LearningCode0
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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