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 401450 of 2964 papers

TitleStatusHype
Label Semantics for Few Shot Named Entity RecognitionCode1
Self-Promoted Supervision for Few-Shot TransformerCode1
Worst Case Matters for Few-Shot RecognitionCode1
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With SupervoxelsCode1
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learningCode1
A Modern Self-Referential Weight Matrix That Learns to Modify ItselfCode1
Bias-Eliminated Semantic Refinement for Any-Shot LearningCode1
Generating Training Data with Language Models: Towards Zero-Shot Language UnderstandingCode1
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot DifficultyCode1
Similarity Learning based Few Shot Learning for ECG Time Series ClassificationCode1
Mobile Robot Manipulation using Pure Object DetectionCode1
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequencesCode1
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and LanguagesCode1
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple IngredientsCode1
From Examples to Rules: Neural Guided Rule Synthesis for Information ExtractionCode1
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot LearningCode1
Resolving Camera Position for a Practical Application of Gaze Estimation on Edge DevicesCode1
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot LearningCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
Recursive Least-Squares Estimator-Aided Online Learning for Visual TrackingCode1
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud SegmentationCode1
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learningCode1
Few-shot Learning with Multilingual Language ModelsCode1
Shaping Visual Representations with Attributes for Few-Shot RecognitionCode1
Learning Instance and Task-Aware Dynamic Kernels for Few Shot LearningCode1
Label Hallucination for Few-Shot ClassificationCode1
PointCLIP: Point Cloud Understanding by CLIPCode1
Linear algebra with transformersCode1
CoNeRF: Controllable Neural Radiance FieldsCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
PartImageNet: A Large, High-Quality Dataset of PartsCode1
POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples NeurIPS 2021Code1
Zero-Shot Learning in Named-Entity Recognition with External KnowledgeCode1
Feature Generation for Long-tail ClassificationCode1
SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot LearningCode1
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
CLUES: Few-Shot Learning Evaluation in Natural Language UnderstandingCode1
An Explanation of In-context Learning as Implicit Bayesian InferenceCode1
Towards Realistic Few-Shot Relation ExtractionCode1
TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text ClassificationCode1
Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat MinimaCode1
MetaICL: Learning to Learn In ContextCode1
Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D PoseCode1
Non-Gaussian Gaussian Processes for Few-Shot RegressionCode1
On sensitivity of meta-learning to support dataCode1
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot GenerationCode1
MaskSplit: Self-supervised Meta-learning for Few-shot Semantic SegmentationCode1
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot LearningCode1
Hyperseed: Unsupervised Learning with Vector Symbolic ArchitecturesCode1
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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