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

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Showing 2601–2650 of 2964 papers

TitleStatusHype
Large Language Models in Finance: A Survey—0
Large Language Models Prompting With Episodic Memory—0
Large Margin Few-Shot Learning—0
Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning—0
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features—0
Large Model for Small Data: Foundation Model for Cross-Modal RF Human Activity Recognition—0
Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery—0
Large-Scale Meta-Learning with Continual Trajectory Shifting—0
LaViP:Language-Grounded Visual Prompts—0
LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning—0
LEA: Meta Knowledge-Driven Self-Attentive Document Embedding for Few-Shot Text Classification—0
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision—0
Learn from Concepts: Towards the Purified Memory for Few-shot Learning—0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
Learning by Examples Based on Multi-level Optimization—0
Learning Chess With Language Models and Transformers—0
Learning Class-level Prototypes for Few-shot Learning—0
Learning Clusterable Visual Features for Zero-Shot Recognition—0
Learning Compositional Representations for Few-Shot Recognition—0
Learning Compositional Representations for Effective Low-Shot Generalization—0
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction—0
Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model—0
Learning Expressive Prompting With Residuals for Vision Transformers—0
Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts—0
Learning from Adversarial Features for Few-Shot Classification—0
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning—0
Learning from Few Samples: A Survey—0
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework—0
Learning from One and Only One Shot—0
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling—0
Learning Graphs for Knowledge Transfer With Limited Labels—0
Learning Human-like Representations to Enable Learning Human Values—0
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding—0
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction—0
Learning More Discriminative Local Descriptors for Few-shot Learning—0
Learning Primitive-aware Discriminative Representations for Few-shot Learning—0
Learning Semantic Similarities for Prototypical Classifiers—0
Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification—0
Learning to Animate Images from A Few Videos to Portray Delicate Human Actions—0
Learning To Avoid Negative Transfer in Few Shot Transfer Learning—0
Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling—0
Learning to Classify Intents and Slot Labels Given a Handful of Examples—0
Learning to Compare Relation: Semantic Alignment for Few-Shot Learning—0
Learning to Control Latent Representations for Few-Shot Learning of Named Entities—0
Learning to Detect Novel and Fine-Grained Acoustic Sequences Using Pretrained Audio Representations—0
Learning to Focus: Cascaded Feature Matching Network for Few-shot Image Recognition—0
Learning To Hallucinate Examples From Extrinsic and Intrinsic Supervision—0
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified