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 25012550 of 2964 papers

TitleStatusHype
Improving Few-Shot Learning through Multi-task Representation Learning TheoryCode0
Unknown Presentation Attack Detection against Rational Attackers0
Data-Efficient Pretraining via Contrastive Self-Supervision0
Cross-Lingual Transfer Learning for Complex Word Identification0
IsOBS: An Information System for Oracle Bone Script0
Few-shot Learning for Time-series Forecasting0
Message Passing Neural Processes0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations0
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization0
Self-supervised Contrastive Zero to Few-shot Learning from Small, Long-tailed Text data0
Improving Few-Shot Visual Classification with Unlabelled Examples0
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms0
Function Contrastive Learning of Transferable Representations0
Sense and Learn: Self-Supervision for Omnipresent Sensors0
Non-greedy Gradient-based Hyperparameter Optimization Over Long Horizons0
A Primal-Dual Subgradient Approachfor Fair Meta LearningCode0
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning0
PennSyn2Real: Training Object Recognition Models without Human Labeling0
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition0
Few-shot learning using pre-training and shots, enriched by pre-trained samples0
Proxy Network for Few Shot LearningCode0
Class Interference Regularization0
All About Knowledge Graphs for Actions0
learn2learn: A Library for Meta-Learning ResearchCode0
Few-Shot Image Classification via Contrastive Self-Supervised Learning0
Few-Shot Learning with Intra-Class Knowledge Transfer0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning0
Dataset Bias in Few-shot Image Recognition0
Compositional Generalization via Neural-Symbolic Stack Machines0
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks0
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems0
Few shot clustering for indoor occupancy detection with extremely low-quality images from battery free camerasCode0
Cooperative Bi-path Metric for Few-shot LearningCode0
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition0
Online Few-shot Gesture Learning on a Neuromorphic Processor0
Attentive Prototype Few-shot Learning with Capsule Network-based Embedding0
Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery0
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation0
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks0
Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment0
Learning from Few Samples: A Survey0
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing0
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning0
Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object DetectionCode0
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning ApproachCode0
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposingCode0
Contextualizing Enhances Gradient Based Meta LearningCode0
Augmented Bi-path Network for Few-shot Learning0
Attentive Graph Neural Networks for Few-Shot Learning0
Towards Cross-Granularity Few-Shot Learning: Coarse-to-Fine Pseudo-Labeling with Visual-Semantic Meta-Embedding0
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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