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 551600 of 2964 papers

TitleStatusHype
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot LearningCode1
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image ClassificationCode1
Few-shot Learning with LSSVM Base Learner and Transductive ModulesCode1
Few-shot Learning with Multilingual Language ModelsCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution AlignmentCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsCode1
Few-shot Natural Language Generation for Task-Oriented DialogCode1
Convolutional Bypasses Are Better Vision Transformer AdaptersCode1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting SystemsCode1
Few-shot Open-set Recognition by Transformation ConsistencyCode1
Few-Shot Recognition via Stage-Wise Retrieval-Augmented FinetuningCode1
Entailment as Few-Shot LearnerCode1
EventCLIP: Adapting CLIP for Event-based Object RecognitionCode1
Few-Shot Unsupervised Continual Learning through Meta-ExamplesCode1
FewVS: A Vision-Semantics Integration Framework for Few-Shot Image ClassificationCode1
Fine-grained Angular Contrastive Learning with Coarse LabelsCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
Covariate Distribution Aware Meta-learningCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
Emoji Attack: A Method for Misleading Judge LLMs in Safety Risk DetectionCode1
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot LearningCode1
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image ClassificationCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
From LSAT: The Progress and Challenges of Complex ReasoningCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Fuzzy Graph Neural Network for Few-Shot LearningCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
Elaborative Rehearsal for Zero-shot Action RecognitionCode1
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute ManipulationCode1
Cross-Domain Few-Shot Learning by Representation FusionCode1
CoNeRF: Controllable Neural Radiance FieldsCode1
Generative Pretrained Hierarchical Transformer for Time Series ForecastingCode1
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot LearningCode1
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental LearningCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language ModelsCode1
Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image ClassificationCode1
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate RepresentationCode1
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese NetworkCode1
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPCode1
Enhancing Few-shot Image Classification with Cosine TransformerCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many ClassesCode1
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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