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
Few-Shot Learning with Visual Distribution Calibration and Cross-Modal Distribution AlignmentCode1
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
Make Prompt-based Black-Box Tuning Colorful: Boosting Model Generalization from Three Orthogonal PerspectivesCode1
Meta Omnium: A Benchmark for General-Purpose Learning-to-LearnCode1
CodeIE: Large Code Generation Models are Better Few-Shot Information ExtractorsCode1
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual NoiseCode1
Multistage Relation Network With Dual-Metric for Few-Shot Hyperspectral Image ClassificationCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Context-enriched molecule representations improve few-shot drug discoveryCode1
Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph RefinementCode1
MasakhaNEWS: News Topic Classification for African languagesCode1
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot LearningCode1
Meta-Learning with a Geometry-Adaptive PreconditionerCode1
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior RefinementCode1
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
What Makes for Effective Few-shot Point Cloud Classification?Code1
Point2Vec for Self-Supervised Representation Learning on Point CloudsCode1
Improving Large Language Models for Clinical Named Entity Recognition via Prompt EngineeringCode1
Supervised Masked Knowledge Distillation for Few-Shot TransformersCode1
Semantic Prompt for Few-Shot Image RecognitionCode1
Few Shot Medical Image Segmentation with Cross Attention TransformerCode1
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical EmbeddingsCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
Prismer: A Vision-Language Model with Multi-Task ExpertsCode1
STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled TablesCode1
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot LearningCode1
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient RepresentationsCode1
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot LearningCode1
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
Meta-Learning Siamese Network for Few-Shot Text ClassificationCode1
Revisiting Discriminative vs. Generative Classifiers: Theory and ImplicationsCode1
Language Quantized AutoEncoders: Towards Unsupervised Text-Image AlignmentCode1
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary DataCode1
IM-IAD: Industrial Image Anomaly Detection Benchmark in ManufacturingCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context LearningCode1
Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpaCode1
Open-Set Likelihood Maximization for Few-Shot LearningCode1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIPCode1
See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual ReasoningCode1
Exploring Efficient Few-shot Adaptation for Vision TransformersCode1
Revisiting Prototypical Network for Cross Domain Few-Shot LearningCode1
ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud ClassificationCode1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
Class-Aware Patch Embedding Adaptation for Few-Shot Image ClassificationCode1
Large Language Models are Better Reasoners with Self-VerificationCode1
Z-ICL: Zero-Shot In-Context Learning with Pseudo-DemonstrationsCode1
Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning ModelsCode1
Federated Few-Shot Learning for Mobile NLPCode1
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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