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 51100 of 2964 papers

TitleStatusHype
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network GenerationCode2
BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical DomainsCode2
Large Language Models to Enhance Bayesian OptimizationCode2
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health RecordsCode2
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following StrategiesCode2
nnSAM: Plug-and-play Segment Anything Model Improves nnUNet PerformanceCode2
Distilled Feature Fields Enable Few-Shot Language-Guided ManipulationCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuningCode2
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-TuningCode2
Improving Factuality and Reasoning in Language Models through Multiagent DebateCode2
PointGPT: Auto-regressively Generative Pre-training from Point CloudsCode2
Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with TextCode2
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token MatchingCode2
ReVersion: Diffusion-Based Relation Inversion from ImagesCode2
Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot LearnersCode2
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
An Empirical Evaluation of Using Large Language Models for Automated Unit Test GenerationCode2
Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal ModelsCode2
Hungry Hungry Hippos: Towards Language Modeling with State Space ModelsCode2
Continual Training of Language Models for Few-Shot LearningCode2
Advancing Plain Vision Transformer Towards Remote Sensing Foundation ModelCode2
Atlas: Few-shot Learning with Retrieval Augmented Language ModelsCode2
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
Language Model CascadesCode2
Few-Shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext TasksCode2
DCT-Net: Domain-Calibrated Translation for Portrait StylizationCode2
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement LearningCode2
Neural Prompt SearchCode2
Large Language Models are Zero-Shot ReasonersCode2
BBTv2: Towards a Gradient-Free Future with Large Language ModelsCode2
mGPT: Few-Shot Learners Go MultilingualCode2
In-BoXBART: Get Instructions into Biomedical Multi-Task LearningCode2
PaLM: Scaling Language Modeling with PathwaysCode2
Masked Autoencoders for Point Cloud Self-supervised LearningCode2
CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech EditingCode2
Cedille: A large autoregressive French language modelCode2
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language ModelsCode2
LibFewShot: A Comprehensive Library for Few-shot LearningCode2
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained TransformersCode2
Feature Learning in Infinite-Width Neural NetworksCode2
FewJoint: A Few-shot Learning Benchmark for Joint Language UnderstandingCode2
Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural NetsCode2
Big Transfer (BiT): General Visual Representation LearningCode2
Prototypical Networks for Few-shot LearningCode2
Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential DataCode1
MOLE: Metadata Extraction and Validation in Scientific Papers Using LLMsCode1
Object-level Cross-view Geo-localization with Location Enhancement and Multi-Head Cross AttentionCode1
MetaGen Blended RAG: Higher Accuracy for Domain-Specific Q&A Without Fine-TuningCode1
Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image ClassificationCode1
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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