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 451500 of 2964 papers

TitleStatusHype
Hyperseed: Unsupervised Learning with Vector Symbolic ArchitecturesCode1
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5Code1
Can Explanations Be Useful for Calibrating Black Box Models?Code1
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot LearnersCode1
Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot LearningCode1
Meta-Learning with Task-Adaptive Loss Function for Few-Shot LearningCode1
Sparse MoEs meet Efficient EnsemblesCode1
Weak Novel Categories without Tears: A Survey on Weak-Shot LearningCode1
Revisiting Self-Training for Few-Shot Learning of Language ModelCode1
RAFT: A Real-World Few-Shot Text Classification BenchmarkCode1
Template-free Prompt Tuning for Few-shot NERCode1
Sparse Spatial Transformers for Few-Shot LearningCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese PoemsCode1
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical NetworksCode1
Learning Opinion Summarizers by Selecting Informative ReviewsCode1
PPT: Pre-trained Prompt Tuning for Few-shot LearningCode1
Discrete and Soft Prompting for Multilingual ModelsCode1
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text modelsCode1
Robust Retrieval Augmented Generation for Zero-shot Slot FillingCode1
Want To Reduce Labeling Cost? GPT-3 Can HelpCode1
Semi-Supervised Exaggeration Detection of Health Science Press ReleasesCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Binocular Mutual Learning for Improving Few-shot ClassificationCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without ForgettingCode1
AdapterHub Playground: Simple and Flexible Few-Shot Learning with AdaptersCode1
Program Synthesis with Large Language ModelsCode1
FlipDA: Effective and Robust Data Augmentation for Few-Shot LearningCode1
Prototype Completion for Few-Shot LearningCode1
Deep Metric Learning for Open World Semantic SegmentationCode1
Noisy Channel Language Model Prompting for Few-Shot Text ClassificationCode1
Elaborative Rehearsal for Zero-shot Action RecognitionCode1
From LSAT: The Progress and Challenges of Complex ReasoningCode1
Recurrent Mask Refinement for Few-Shot Medical Image SegmentationCode1
Few-Shot and Continual Learning with Attentive Independent MechanismsCode1
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target DataCode1
Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report GenerationCode1
Modelling Latent Translations for Cross-Lingual TransferCode1
ProtoTransformer: A Meta-Learning Approach to Providing Student FeedbackCode1
Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text RecognitionCode1
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy DataCode1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Rectifying the Shortcut Learning of Background for Few-Shot LearningCode1
FewCLUE: A Chinese Few-shot Learning Evaluation BenchmarkCode1
FLEX: Unifying Evaluation for Few-Shot NLPCode1
Leveraging Hierarchical Structures for Few-Shot Musical Instrument RecognitionCode1
Sequential Recommendation for Cold-start Users with Meta Transitional LearningCode1
Few-Shot Learning with a Strong TeacherCode1
Cross-domain Few-shot Learning with Task-specific AdaptersCode1
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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