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

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Showing 1151–1200 of 2964 papers

TitleStatusHype
Identifying and Analyzing Task-Encoding Tokens in Large Language Models—0
A Survey of Diffusion Models in Natural Language Processing—0
Differentially Private Meta-Learning—0
Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?—0
Graph Machine Learning in the Era of Large Language Models (LLMs)—0
Hierarchical end-to-end autonomous navigation through few-shot waypoint detection—0
Differentially Private In-context Learning via Sampling Few-shot Mixed with Zero-shot Outputs—0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation—0
Bias Testing and Mitigation in LLM-based Code Generation—0
Differentiable Entailment for Parameter Efficient Few Shot Learning—0
Analysis and Applications of Deep Learning with Finite Samples in Full Life-Cycle Intelligence of Nuclear Power Generation—0
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models—0
Cap2Aug: Caption guided Image to Image data Augmentation—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
AdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies—0
Analogy-Forming Transformers for Few-Shot 3D Parsing—0
Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning—0
Gradual Relation Network: Decoding Intuitive Upper Extremity Movement Imaginations Based on Few-Shot EEG Learning—0
DGP-Net: Dense Graph Prototype Network for Few-Shot SAR Target Recognition—0
Beyond Linearity: Squeeze-and-Recalibrate Blocks for Few-Shot Whole Slide Image Classification—0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities—0
A3: Few-shot Prompt Learning of Unlearnable Examples with Cross-Modal Adversarial Feature Alignment—0
Detecting Endangered Marine Species in Autonomous Underwater Vehicle Imagery Using Point Annotations and Few-Shot Learning—0
Beyond Few-shot Object Detection: A Detailed Survey—0
Detecting Actionable Requests and Offers on Social Media During Crises Using LLMs—0
Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction—0
An Adaptive Plug-and-Play Network for Few-Shot Learning—0
Gradient-Based Meta-Learning Using Uncertainty to Weigh Loss for Few-Shot Learning—0
Grammatical information in BERT sentence embeddings as two-dimensional arrays—0
Designing Informative Metrics for Few-Shot Example Selection—0
Beyond Deepfake Images: Detecting AI-Generated Videos—0
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN—0
Beyond Data Scarcity: A Frequency-Driven Framework for Zero-Shot Forecasting—0
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery—0
Dependable Neural Networks for Safety Critical Tasks—0
Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models—0
Dental Severity Assessment through Few-shot Learning and SBERT Fine-tuning—0
Dense Classification and Implanting for Few-Shot Learning—0
Better (pseudo-)labels for semi-supervised instance segmentation—0
A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs—0
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees—0
Demystifying Prompts in Language Models via Perplexity Estimation—0
Demystification of Few-shot and One-shot Learning—0
DemoShapley: Valuation of Demonstrations for In-Context Learning—0
AMP0: Species-Specific Prediction of Anti-microbial Peptides using Zero and Few Shot Learning—0
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning—0
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration—0
A Comprehensive Review of Few-shot Action Recognition—0
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts—0
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All datasetsMedConceptsQADTDFGVC-AircraftMini-ImageNet - 5-Shot LearningMini-Imagenet 5-way (1-shot)Stanford CarsMini-ImageNet - 1-Shot LearningPubMedQACaltech101CaseHOLDEuroSATFlowers-102

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified