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 501550 of 2964 papers

TitleStatusHype
Detecting Hate Speech with GPT-3Code1
ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain RetrievalCode1
Diagnosing Infeasible Optimization Problems Using Large Language ModelsCode1
Prototype Completion for Few-Shot LearningCode1
Extending Context Window of Large Language Models via Semantic CompressionCode1
Example-Based Named Entity RecognitionCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a DifferenceCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
Compressing Lengthy Context With UltraGistCode1
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
FAITH: Few-Shot Graph Classification with Hierarchical Task GraphsCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
Enhancing Information Maximization with Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot LearningCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
Emoji Attack: A Method for Misleading Judge LLMs in Safety Risk DetectionCode1
Elaborative Rehearsal for Zero-shot Action RecognitionCode1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
EventCLIP: Adapting CLIP for Event-based Object RecognitionCode1
FAPIS: A Few-shot Anchor-free Part-based Instance SegmenterCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many ClassesCode1
Easter2.0: Improving convolutional models for handwritten text recognitionCode1
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple IngredientsCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
Consistency-guided Prompt Learning for Vision-Language ModelsCode1
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language ModelsCode1
Constellation Nets for Few-Shot LearningCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
Enhancing Few-shot Image Classification with Cosine TransformerCode1
Entailment as Few-Shot LearnerCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Context-enriched molecule representations improve few-shot drug discoveryCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
Explanation-Guided Training for Cross-Domain Few-Shot ClassificationCode1
Exploring Efficient Few-shot Adaptation for Vision TransformersCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Convolutional Bypasses Are Better Vision Transformer AdaptersCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
Feature Generation for Long-tail ClassificationCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian RegularizationCode1
CoNeRF: Controllable Neural Radiance FieldsCode1
DPGN: Distribution Propagation Graph Network for Few-shot 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