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
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution PredictionCode0
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline TimepointCode0
Weak Novel Categories without Tears: A Survey on Weak-Shot LearningCode1
Multi-Objective Few-shot Learning for Fair Classification0
Task Affinity with Maximum Bipartite Matching in Few-Shot LearningCode0
Revisiting Self-Training for Few-Shot Learning of Language ModelCode1
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis0
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing0
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING0
Few-shot Learning with Big Prototypes0
Invariance-Guided Feature Evolution for Few-Shot Learning0
A theoretically grounded characterization of feature representations0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification0
Few-shot graph link prediction with domain adaptation0
Linear algebra with transformers0
Hierarchical Cross Contrastive Learning of Visual Representations0
Robust Cross-Modal Semi-supervised Few Shot Learning0
Refining Multimodal Representations using a modality-centric self-supervised module0
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification0
Loss meta-learning for forecasting0
Assessing two novel distance-based loss functions for few-shot image classification0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning0
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning0
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics0
Neural Variational Dropout Processes0
Meta Learning with Minimax Regularization0
Generalized Sampling Method for Few Shot Learning0
Few-Shot Multi-task Learning via Implicit regularization0
Dataset Bias Prediction for Few-Shot Image Classification0
HyperTransformer: Attention-Based CNN Model Generation from Few Samples0
Few-shot Learning via Dirichlet Tessellation Ensemble0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation0
Generalization Bounds For Meta-Learning: An Information-Theoretic AnalysisCode0
Template-free Prompt Tuning for Few-shot NERCode1
RAFT: A Real-World Few-Shot Text Classification BenchmarkCode1
Multimodality in Meta-Learning: A Comprehensive Survey0
Sparse Spatial Transformers for Few-Shot LearningCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data0
Towards Generalized and Incremental Few-Shot Object Detection0
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese PoemsCode1
Learning by Examples Based on Multi-level Optimization0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications0
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical NetworksCode1
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning0
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning0
Reframing Instructional Prompts to GPTk's Language0
Partner-Assisted Learning for Few-Shot Image Classification0
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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