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 28512900 of 2964 papers

TitleStatusHype
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation0
LaSO: Label-Set Operations networks for multi-label few-shot learningCode0
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency ParsingCode0
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-TimeCode0
Adaptive Cross-Modal Few-Shot LearningCode0
Infinite Mixture Prototypes for Few-Shot Learning0
Meta-CurvatureCode0
Adaptive Posterior Learning: few-shot learning with a surprise-based memory moduleCode0
Centroid-based deep metric learning for speaker recognition0
Massively Multilingual Transfer for NERCode0
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text ClassificationCode0
Few-shot Learning with Meta Metric Learners0
Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts0
Sequential Skip Prediction with Few-shot in Streamed Music ContentsCode0
FIGR: Few-shot Image Generation with ReptileCode0
Low-Shot Learning from Imaginary 3D Model0
An Investigation of Few-Shot Learning in Spoken Term ClassificationCode0
Meta Architecture SearchCode0
Learning Compositional Representations for Few-Shot Recognition0
Reconciling meta-learning and continual learning with online mixtures of tasks0
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning0
The effects of negative adaptation in Model-Agnostic Meta-Learning0
Generalized Zero- and Few-Shot Learning via Aligned Variational AutoencodersCode0
Cross-Modulation Networks for Few-Shot Learning0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
MetaGAN: An Adversarial Approach to Few-Shot Learning0
Unsupervised Meta-Learning For Few-Shot Image Classification0
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained EnvironmentsCode0
Self Paced Adversarial Training for Multimodal Few-shot Learning0
Representation based and Attention augmented Meta learning0
RelationNet2: Deep Comparison Columns for Few-Shot LearningCode0
Few-shot Learning for Named Entity Recognition in Medical TextCode0
Power Normalizing Second-order Similarity Network for Few-shot Learning0
Few-shot learning with attention-based sequence-to-sequence models0
Class-Agnostic CountingCode0
Gaussian Process Conditional Density Estimation0
Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model0
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art EvaluationCode0
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning0
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning0
Incremental Few-Shot Learning with Attention Attractor NetworksCode0
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data ScenarioCode0
Task-Embedded Control Networks for Few-Shot Imitation LearningCode0
Open-Ended Content-Style Recombination Via Leakage Filtering0
Variadic Learning by Bayesian Nonparametric Deep Embedding0
Hierarchy-based Image Embeddings for Semantic Image RetrievalCode0
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing0
Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond0
Dynamic Conditional Networks for Few-Shot Learning0
Few-Shot Human Motion Prediction via Meta-Learning0
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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