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 21512200 of 2964 papers

TitleStatusHype
Few-shot Intent Classification and Slot Filling with Retrieved Examples0
Contextual HyperNetworks for Novel Feature Adaptation0
How Sensitive are Meta-Learners to Dataset Imbalance?Code1
On Unifying Misinformation DetectionCode0
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency ParsingCode0
Reinforced Attention for Few-Shot Learning and Beyond0
ORBIT: A Real-World Few-Shot Dataset for Teachable Object RecognitionCode1
Few-Shot Action Recognition with Compromised Metric via Optimal Transport0
SiT: Self-supervised vIsion TransformerCode1
Towards Enabling Meta-Learning from Target ModelsCode0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark0
Efficient Personalized Speech Enhancement through Self-Supervised Learning0
Towards Offensive Language Identification for Dravidian LanguagesCode0
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool0
Federated Few-Shot Learning with Adversarial Learning0
Modular Adaptation for Cross-Domain Few-Shot LearningCode0
FAPIS: A Few-shot Anchor-free Part-based Instance SegmenterCode1
Few-shot learning through contextual data augmentationCode0
Head2HeadFS: Video-based Head Reenactment with Few-shot Learning0
Pre-training strategies and datasets for facial representation learningCode1
von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised LearningCode1
Explaining Representation by Mutual Information0
IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT0
When Few-Shot Learning Meets Video Object Detection0
MetaNODE: Prototype Optimization as a Neural ODE for Few-Shot LearningCode1
Spirit Distillation: Precise Real-time Semantic Segmentation of Road Scenes with Insufficient Data0
Learning Dynamic Alignment via Meta-filter for Few-shot LearningCode1
Orthogonal Projection LossCode1
Detecting Hate Speech with GPT-3Code1
Prototypical Representation Learning for Relation ExtractionCode1
Improving and Simplifying Pattern Exploiting TrainingCode1
Multi-level Metric Learning for Few-shot Image RecognitionCode0
Real-Time Visual Object Tracking via Few-Shot Learning0
A New Method for Features Normalization in Motor Imagery Few-Shot Learning using Resting-State0
Towards Few-Shot Fact-Checking via Perplexity0
Repurposing Pretrained Models for Robust Out-of-domain Few-Shot LearningCode0
DMN4: Few-shot Learning via Discriminative Mutual Nearest Neighbor Neural Network0
Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video0
A Few-Shot Learning Approach for Accelerated MRI via Fusion of Data-Driven and Subject-Driven Priors0
Inductive Relation Prediction by BERTCode1
FSCE: Few-Shot Object Detection via Contrastive Proposal EncodingCode1
Multi-Pretext Attention Network for Few-shot Learning with Self-supervisionCode0
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data0
Few-shot Learning for Slot Tagging with Attentive Relational Network0
Few-shot Open-set Recognition by Transformation ConsistencyCode1
A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics0
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
OmniNet: Omnidirectional Representations from TransformersCode0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection0
Domain Adaptation for Learning Generator from Paired Few-Shot Data0
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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