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 19011950 of 2964 papers

TitleStatusHype
Privacy Policy Analysis through Prompt Engineering for LLMs0
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification0
Probabilistic Model-Agnostic Meta-Learning0
Proceedings of the ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts0
ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition0
Progressive Cluster Purification for Transductive Few-shot Learning0
Projective Subspace Networks For Few-Shot Learning0
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting0
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models0
Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning0
Prompt Combines Paraphrase: Enhancing Biomedical “Pre-training, Prompt and Predicting” Models by Explaining Rare Biomedical Concepts0
Prompt Diffusion Robustifies Any-Modality Prompt Learning0
Prompt-free and Efficient Language Model Fine-Tuning0
Prompt-Guided Few-Shot Event Detection0
Prompting Decision Transformer for Few-Shot Policy Generalization0
Prompting through Prototype: A Prototype-based Prompt Learning on Pretrained Vision-Language Models0
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks0
ProQA: Structural Prompt-based Pre-training for Unified Question Answering0
ProtoGAN: Towards Few Shot Learning for Action Recognition0
Proto-OOD: Enhancing OOD Object Detection with Prototype Feature Similarity0
Prototype-Based Approach for One-Shot Segmentation of Brain Tumors using Few-Shot Learning0
PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification0
Prototype Optimization with Neural ODE for Few-Shot Learning0
Prototype Rectification for Few-Shot Learning0
Prototypes-oriented Transductive Few-shot Learning with Conditional Transport0
Prototypical Bregman Networks0
Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption0
Prototypical Representation Learning for Low-resource Knowledge Extraction: Summary and Perspective0
Prototypical Verbalizer for Prompt-based Few-shot Tuning0
Pseudo-Loss Confidence Metric for Semi-Supervised Few-Shot Learning0
PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning0
Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency0
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms0
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries0
Quantum Diffusion Models for Few-Shot Learning0
RA-DIT: Retrieval-Augmented Dual Instruction Tuning0
RAGSys: Item-Cold-Start Recommender as RAG System0
Ranking Distance Calibration for Cross-Domain Few-Shot Learning0
Rapid Model Architecture Adaption for Meta-Learning0
Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark0
Flame-state monitoring based on very low number of visible or infrared images via few-shot learning0
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators0
Real-Time Visual Object Tracking via Few-Shot Learning0
Reasoning with Large Language Models, a Survey0
Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning0
Reddit-Impacts: A Named Entity Recognition Dataset for Analyzing Clinical and Social Effects of Substance Use Derived from Social Media0
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models0
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning0
Refining Multimodal Representations using a modality-centric self-supervised module0
Reframing Instructional Prompts to GPTk's Language0
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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