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 251300 of 2964 papers

TitleStatusHype
Group Preference Optimization: Few-Shot Alignment of Large Language ModelsCode1
Harvesting and Refining Question-Answer Pairs for Unsupervised QACode1
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot GenerationCode1
Holistic Semantic Representation for Navigational Trajectory GenerationCode1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many ClassesCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Adaptive Subspaces for Few-Shot LearningCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
Exploring Efficient Few-shot Adaptation for Vision TransformersCode1
Few-shot Learner Parameterization by Diffusion Time-stepsCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
Domain-Adaptive Few-Shot LearningCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph PretrainingCode1
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the CentroidCode1
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal PerspectiveCode1
BOIL: Towards Representation Change for Few-shot LearningCode1
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot LearningCode1
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual NoiseCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
Discrete and Soft Prompting for Multilingual ModelsCode1
DETReg: Unsupervised Pretraining with Region Priors for Object DetectionCode1
Detecting Hate Speech with GPT-3Code1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language InferenceCode1
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
Deep Metric Learning for Open World Semantic SegmentationCode1
DenoiSeg: Joint Denoising and SegmentationCode1
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot LearningCode1
RARR: Researching and Revising What Language Models Say, Using Language ModelsCode1
Defining Benchmarks for Continual Few-Shot LearningCode1
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and SynthesisCode1
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy DataCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Diagnosing Infeasible Optimization Problems Using Large Language ModelsCode1
DiffCLIP: Few-shot Language-driven Multimodal ClassifierCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
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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