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 25512600 of 2964 papers

TitleStatusHype
Improving Social Meaning Detection with Pragmatic Masking and Surrogate Fine-TuningCode0
Meta-Generating Deep Attentive Metric for Few-shot ClassificationCode0
Meta-GNN: On Few-shot Node Classification in Graph Meta-learningCode0
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot ExamplesCode0
Data-Efficient Language Shaped Few-shot Image ClassificationCode0
Rethinking Skill Extraction in the Job Market Domain using Large Language ModelsCode0
MetaKernel: Learning Variational Random Features with Limited LabelsCode0
Transductive Few-Shot Classification on the Oblique ManifoldCode0
Data-Efficient Classification of Radio GalaxiesCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Meta-learning algorithms for Few-Shot Computer VisionCode0
Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised LearningCode0
The Role of Data Curation in Image CaptioningCode0
SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State TrackingCode0
Meta-learning for Classifying Previously Unseen Data Source into Previously Unseen Emotional CategoriesCode0
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency ParsingCode0
An Investigation of Few-Shot Learning in Spoken Term ClassificationCode0
Are LSTMs Good Few-Shot Learners?Code0
Data Augmentation Generative Adversarial NetworksCode0
Are Large Language Models Robust Coreference Resolvers?Code0
APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World AgentsCode0
Meta-Learning Initializations for Image SegmentationCode0
Improving Meta-Learning Generalization with Activation-Based Early-StoppingCode0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Bootstrapped Meta-LearningCode0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
Improving generalization in large language models by learning prefix subspacesCode0
A Primal-Dual Subgradient Approachfor Fair Meta LearningCode0
Meta-Learning Probabilistic Inference For PredictionCode0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement LearningCode0
When Does Self-supervision Improve Few-shot Learning?Code0
Improved Visually Prompted Keyword Localisation in Real Low-Resource SettingsCode0
Improved transferability of self-supervised learning models through batch normalization finetuningCode0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language TasksCode0
Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer ModelsCode0
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained EnvironmentsCode0
Revisiting Automated Prompting: Are We Actually Doing Better?Code0
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event DetectionCode0
HyperPlanes: Hypernetwork Approach to Rapid NeRF AdaptationCode0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
Execution-Based Evaluation of Natural Language to Bash and PowerShell for Incident RemediationCode0
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point CloudCode0
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph CompletionCode0
HQP: A Human-Annotated Dataset for Detecting Online PropagandaCode0
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror DescentCode0
Meta-Learning with Warped Gradient DescentCode0
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation for ClassificationCode0
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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