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 301350 of 2964 papers

TitleStatusHype
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot LearningCode1
Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real WorldCode1
RARR: Researching and Revising What Language Models Say, Using Language ModelsCode1
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many ClassesCode1
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object InteractionsCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language ModelsCode1
Emoji Attack: A Method for Misleading Judge LLMs in Safety Risk DetectionCode1
Automatic Label Sequence Generation for Prompting Sequence-to-sequence ModelsCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Entailment as Few-Shot LearnerCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground AlignmentCode1
EventCLIP: Adapting CLIP for Event-based Object RecognitionCode1
Example-Based Named Entity RecognitionCode1
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
Adaptive Subspaces for Few-Shot LearningCode1
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal PerspectiveCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language ModelsCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
BankNote-Net: Open dataset for assistive universal currency recognitionCode1
Extending Context Window of Large Language Models via Semantic CompressionCode1
Feature Generation for Long-tail ClassificationCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
FETA: Towards Specializing Foundation Models for Expert Task ApplicationsCode1
BECLR: Batch Enhanced Contrastive Few-Shot LearningCode1
FewSAR: A Few-shot SAR Image Classification BenchmarkCode1
Few-shot Adaptation Works with UnpredicTable DataCode1
A Modern Self-Referential Weight Matrix That Learns to Modify ItselfCode1
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy DataCode1
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERTCode1
Few-Shot Diffusion ModelsCode1
Better Few-Shot Relation Extraction with Label Prompt DropoutCode1
Better Generalized Few-Shot Learning Even Without Base DataCode1
Few-Shot Learning by Dimensionality Reduction in Gradient SpaceCode1
Few-Shot Learning by Integrating Spatial and Frequency RepresentationCode1
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
Few-Shot Learning for Opinion SummarizationCode1
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and SynthesisCode1
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral MeasuresCode1
Few-Shot Learning with a Strong TeacherCode1
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image ClassificationCode1
DenoiSeg: Joint Denoising and SegmentationCode1
Few-shot Learning with Multilingual Language ModelsCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
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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