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 201250 of 2964 papers

TitleStatusHype
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot LearningCode1
Hyperbolic Uncertainty-Aware Few-Shot Incremental Point Cloud Segmentation0
A3: Few-shot Prompt Learning of Unlearnable Examples with Cross-Modal Adversarial Feature Alignment0
Adaptive Parameter Selection for Tuning Vision-Language Models0
MODA: Motion-Drift Augmentation for Inertial Human Motion Analysis0
Text Augmented Correlation Transformer For Few-shot Classification & Segmentation0
Cross-Modal Mapping: Mitigating the Modality Gap for Few-Shot Image Classification0
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning0
DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation0
Neural Conformal Control for Time Series ForecastingCode1
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation0
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic SegmentationCode1
MVREC: A General Few-shot Defect Classification Model Using Multi-View Region-ContextCode1
UNEM: UNrolled Generalized EM for Transductive Few-Shot LearningCode0
LEARN: A Unified Framework for Multi-Task Domain Adapt Few-Shot LearningCode0
The Role of Recurrency in Image Segmentation for Noisy and Limited Sample Settings0
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations0
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning0
Enhancing Masked Time-Series Modeling via Dropping PatchesCode0
Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning0
Aspect-Based Few-Shot Learning0
NAVCON: A Cognitively Inspired and Linguistically Grounded Corpus for Vision and Language Navigation0
CRoF: CLIP-based Robust Few-shot Learning on Noisy Labels0
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&ACode0
How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach0
SAMIC: Segment Anything with In-Context Spatial Prompt Engineering0
Text and Image Are Mutually Beneficial: Enhancing Training-Free Few-Shot Classification with CLIPCode1
Do Tutors Learn from Equity Training and Can Generative AI Assess It?Code0
SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering0
SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot LearningCode0
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI0
Kajal: Extracting Grammar of a Source Code Using Large Language ModelsCode0
All You Need in Knowledge Distillation Is a Tailored Coordinate System0
DiffCLIP: Few-shot Language-driven Multimodal ClassifierCode1
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language ModelCode0
ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)Code0
Flexible and Scalable Deep Dendritic Spiking Neural Networks with Multiple Nonlinear Branching0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation0
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks0
Diversity Over Quantity: A Lesson From Few Shot Relation Classification0
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning0
A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities0
KNN-MMD: Cross Domain Wireless Sensing via Local Distribution AlignmentCode1
Evolutionary Pre-Prompt Optimization for Mathematical Reasoning0
The broader spectrum of in-context learning0
ALMA: Alignment with Minimal Annotation0
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs0
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
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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