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 2151–2200 of 2964 papers

TitleStatusHype
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning—0
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers—0
Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLICode0
A Closer Look at Prototype Classifier for Few-shot Image Classification—0
Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models—0
Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease ClassificationCode0
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
Multi-Objective Few-shot Learning for Fair Classification—0
Task Affinity with Maximum Bipartite Matching in Few-Shot LearningCode0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing—0
Few-shot Learning with Big Prototypes—0
Loss meta-learning for forecasting—0
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification—0
Few-shot Learning via Dirichlet Tessellation Ensemble—0
Refining Multimodal Representations using a modality-centric self-supervised module—0
Linear algebra with transformers—0
Dataset Bias Prediction for Few-Shot Image Classification—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Hierarchical Cross Contrastive Learning of Visual Representations—0
Few-shot graph link prediction with domain adaptation—0
Generalization Bounds For Meta-Learning: An Information-Theoretic AnalysisCode0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics—0
Robust Cross-Modal Semi-supervised Few Shot Learning—0
Invariance-Guided Feature Evolution for Few-Shot Learning—0
Neural Variational Dropout Processes—0
MDFL: A UNIFIED FRAMEWORK WITH META-DROPOUT FOR FEW-SHOT LEARNING—0
Generalized Sampling Method for Few Shot Learning—0
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning—0
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification—0
Few-Shot Multi-task Learning via Implicit regularization—0
A theoretically grounded characterization of feature representations—0
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning—0
MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation—0
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation—0
Revisiting Linear Decision Boundaries for Few-Shot Learning with Transformer Hypernetworks—0
HyperTransformer: Attention-Based CNN Model Generation from Few Samples—0
Meta Learning with Minimax Regularization—0
Multimodality in Meta-Learning: A Comprehensive Survey—0
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data—0
Towards Generalized and Incremental Few-Shot Object Detection—0
Learning by Examples Based on Multi-level Optimization—0
Multi-Domain Few-Shot Learning and Dataset for Agricultural Applications—0
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning—0
Reframing Instructional Prompts to GPTk's Language—0
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning—0
Partner-Assisted Learning for Few-Shot Image Classification—0
Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation—0
One-Class Meta-Learning: Towards Generalizable Few-Shot Open-Set Classification—0
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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