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 1001–1050 of 2964 papers

TitleStatusHype
Empirical Evaluation of Topic Zero- and Few-Shot Learning for Stance Dissonance Detection—0
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry—0
Few-Shot Few-Shot Learning and the role of Spatial Attention—0
Few-shot fine-tuning SOTA summarization models for medical dialogues—0
Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification—0
Few-shot graph link prediction with domain adaptation—0
CalliffusionV2: Personalized Natural Calligraphy Generation with Flexible Multi-modal Control—0
Compositional Fine-Grained Low-Shot Learning—0
Few-Shot Human Motion Prediction via Meta-Learning—0
Few-Shot Image Classification Along Sparse Graphs—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion—0
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation—0
EMO: Episodic Memory Optimization for Few-Shot Meta-Learning—0
Few-Shot In-Context Imitation Learning via Implicit Graph Alignment—0
Few-Shot Incremental Learning for Label-to-Image Translation—0
Few-shot Intent Classification and Slot Filling with Retrieved Examples—0
Few-Shot Knowledge Graph Completion with Data Fusion and Augmentation—0
Calibrated neighborhood aware confidence measure for deep metric learning—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images—0
Embedding Space Allocation with Angle-Norm Joint Classifiers for Few-Shot Class-Incremental Learning—0
Embedding Adaptation is Still Needed for Few-Shot Learning—0
C^*-algebra Net: A New Approach Generalizing Neural Network Parameters to C^*-algebra—0
EICO: Improving Few-Shot Text Classification via Explicit and Implicit Consistency Regularization—0
CAFENet: Class-Agnostic Few-Shot Edge Detection Network—0
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering—0
Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks—0
Advancing Prompt Learning through an External Layer—0
A Baseline for Self-state Identification and Classification in Mental Health Data: CLPsych 2025 Task—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets—0
Efficient Meta Learning via Minibatch Proximal Update—0
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning—0
Cabrita: closing the gap for foreign languages—0
Efficient few-shot learning for pixel-precise handwritten document layout analysis—0
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models—0
TransMed: Large Language Models Enhance Vision Transformer for Biomedical Image Classification—0
Efficient Automatic Meta Optimization Search for Few-Shot Learning—0
Budget-aware Few-shot Learning via Graph Convolutional Network—0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State—0
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning—0
Efficacy of Synthetic Data as a Benchmark—0
Effective Transfer of Pretrained Large Visual Model for Fabric Defect Segmentation via Specifc Knowledge Injection—0
FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs—0
A Cross-Lingual Meta-Learning Method Based on Domain Adaptation for Speech Emotion Recognition—0
Effective Few-Shot Classification with Transfer Learning—0
Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process—0
EEML: Ensemble Embedded Meta-learning—0
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All datasetsMedConceptsQADTDFGVC-AircraftMini-ImageNet - 5-Shot LearningMini-Imagenet 5-way (1-shot)Stanford CarsMini-ImageNet - 1-Shot LearningPubMedQACaltech101CaseHOLDEuroSATFlowers-102

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