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 23512400 of 2964 papers

TitleStatusHype
Few-shot Multimodal Multitask Multilingual Learning0
Few-Shot Multi-task Learning via Implicit regularization0
Few-Shot Named Entity Recognition: An Empirical Baseline Study0
Few-shot Named Entity Recognition with Cloze Questions0
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness0
Few-Shot Nested Named Entity Recognition0
Few-shot Object Detection on Remote Sensing Images0
Few-Shot Object Detection: Research Advances and Challenges0
Few-Shot Object Detection with Foundation Models0
Few-Shot Object Detection with Proposal Balance Refinement0
Few-Shot Object Recognition from Machine-Labeled Web Images0
Few-Shot Partial-Label Learning0
Few-Shot Pill Recognition0
Few-shot pixel-precise document layout segmentation via dynamic instance generation and local thresholding0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention0
Few-shot Query-Focused Summarization with Prefix-Merging0
Few Shot Rationale Generation using Self-Training with Dual Teachers0
Few-Shot Regression via Learned Basis Functions0
Few-Shot Regression via Learning Sparsifying Basis Functions0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation0
Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges0
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding0
Few-shot Sequence Learning with Transformers0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network0
Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention0
Few Shot Speaker Recognition using Deep Neural Networks0
Few-Shot Stance Detection via Target-Aware Prompt Distillation0
FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs0
Few-shot time-series anomaly detection with unsupervised domain adaptation0
Few-shot time series segmentation using prototype-defined infinite hidden Markov models0
Few-shot training LLMs for project-specific code-summarization0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency0
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding0
Few-Shot Video Classification via Temporal Alignment0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets0
FGN: Fully Guided Network for Few-Shot Instance Segmentation0
FHIST: A Benchmark for Few-shot Classification of Histological Images0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?0
Financial Knowledge Large Language Model0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction0
Fine-Grained Few Shot Learning with Foreground Object Transformation0
Fine-grained Few-shot Recognition by Deep Object Parsing0
Fine-grained Image-to-Image Transformation towards Visual Recognition0
Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors0
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI0
FLamE: Few-shot Learning from Natural Language Explanations0
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers0
FLEURS-R: A Restored Multilingual Speech Corpus for Generation Tasks0
Flexible and Scalable Deep Dendritic Spiking Neural Networks with Multiple Nonlinear Branching0
Probing Few-Shot Generalization with Attributes0
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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