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 16011650 of 2964 papers

TitleStatusHype
RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for MarioCode0
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification0
Learning More Discriminative Local Descriptors for Few-shot Learning0
Meta-DM: Applications of Diffusion Models on Few-Shot Learning0
Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework0
The Role of Data Curation in Image CaptioningCode0
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified ModelsCode0
Plug-and-Play Multilingual Few-shot Spoken Words Recognition0
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive SummarizationCode0
Learning to Detect Novel and Fine-Grained Acoustic Sequences Using Pretrained Audio Representations0
Causal Interventions-based Few-Shot Named Entity Recognition0
DocLangID: Improving Few-Shot Training to Identify the Language of Historical DocumentsCode0
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph WalkingCode0
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based AlignmentCode0
Accelerating Neural Self-Improvement via Bootstrapping0
HQP: A Human-Annotated Dataset for Detecting Online PropagandaCode0
Analogy-Forming Transformers for Few-Shot 3D Parsing0
Adaptive manifold for imbalanced transductive few-shot learning0
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection0
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework0
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups0
RPLKG: Robust Prompt Learning with Knowledge Graph0
Semantic-Aware Graph Matching Mechanism for Multi-Label Image RecognitionCode0
Clustered-patch Element Connection for Few-shot LearningCode0
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning0
Few-shot Medical Image Segmentation via Cross-Reference Transformer0
CancerGPT: Few-shot Drug Pair Synergy Prediction using Large Pre-trained Language Models0
A Survey on Few-Shot Class-Incremental Learning0
Instance-level Few-shot Learning with Class Hierarchy MiningCode0
The Art of Camouflage: Few-Shot Learning for Animal Detection and SegmentationCode0
SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders0
Out-of-distribution Few-shot Learning For Edge Devices without Model Fine-tuning0
LSFSL: Leveraging Shape Information in Few-shot Learning0
On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence0
Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges0
Simulated Annealing in Early Layers Leads to Better GeneralizationCode0
WikiGoldSK: Annotated Dataset, Baselines and Few-Shot Learning Experiments for Slovak Named Entity RecognitionCode0
Revisiting Automated Prompting: Are We Actually Doing Better?Code0
Learning to Learn with Indispensable Connections0
Sociocultural knowledge is needed for selection of shots in hate speech detection tasks0
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement LearningCode0
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection0
Channel Phase Processing in Wireless Networks for Human Activity Recognition0
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network0
On Codex Prompt Engineering for OCL Generation: An Empirical Study0
Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning0
Learning Expressive Prompting With Residuals for Vision Transformers0
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge DistillationCode0
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization0
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot GeneralizationCode0
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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