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 20512100 of 2964 papers

TitleStatusHype
In-BoXBART: Get Instructions into Biomedical Multi-task Learning0
Multi-level Second-order Few-shot LearningCode0
Learning from One and Only One Shot0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning0
Semantics-driven Attentive Few-shot Learning over Clean and Noisy SamplesCode0
Budget-aware Few-shot Learning via Graph Convolutional Network0
Few-Shot Incremental Learning for Label-to-Image Translation0
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer0
Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning0
Rethinking Video Anomaly Detection - A Continual Learning Approach0
Semi-Supervised Few-Shot Learning via Multi-Factor ClusteringCode0
On the Role of Neural Collapse in Transfer Learning0
Associative Adversarial Learning Based on Selective Attack0
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains0
Does MAML Only Work via Feature Re-use? A Data Centric PerspectiveCode0
The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and their Empirical Equivalence0
3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Naïve0
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction0
DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Images0
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey0
Bioacoustic Event Detection with prototypical networks and data augmentation0
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing0
Hierarchical Variational Memory for Few-shot Learning Across DomainsCode0
Few-shot Multi-hop Question Answering over Knowledge Base0
Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent DetectionCode0
Hybrid Graph Neural Networks for Few-Shot Learning0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning0
Learning from the Tangram to Solve Mini Visual TasksCode0
Which images to label for few-shot medical landmark detection?0
Few-Shot Image Classification Along Sparse Graphs0
A Survey of Deep Learning for Low-Shot Object Detection0
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration0
Adaptive Poincaré Point to Set Distance for Few-Shot Classification0
Prompt-free and Efficient Language Model Fine-Tuning0
Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code0
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?Code0
Re-ranking for image retrieval and transductive few-shot classification0
MDFM: Multi-Decision Fusing Model for Few-Shot Learning0
Learning to Learn Dense Gaussian Processes for Few-Shot Learning0
Ranking Distance Calibration for Cross-Domain Few-Shot Learning0
Statistically and Computationally Efficient Linear Meta-representation Learning0
True Few-Shot Learning with Prompts -- A Real-World Perspective0
Deep Representation Learning with an Information-theoretic Loss0
Coarse-To-Fine Incremental Few-Shot LearningCode0
Deep metric learning improves lab of origin prediction of genetically engineered plasmids0
Few-shot Named Entity Recognition with Cloze Questions0
ShufaNet: Classification method for calligraphers who have reached the professional level0
Adaptive Transfer Learning: a simple but effective transfer learning0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
Prompt Combines Paraphrase: Enhancing Biomedical “Pre-training, Prompt and Predicting” Models by Explaining Rare Biomedical Concepts0
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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