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 14011450 of 2964 papers

TitleStatusHype
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport0
Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEsCode0
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps0
AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage0
Hypernetwork approach to Bayesian MAMLCode1
Bayesian Prompt Learning for Image-Language Model GeneralizationCode1
Uncertainty-Aware Meta-Learning for Multimodal Task DistributionsCode0
Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground AlignmentCode1
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingCode1
LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language ModelsCode1
AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements0
TEAM UFAL @ CreativeSumm 2022: BART and SamSum based few-shot approach for creative Summarization0
PCBERT: Parent and Child BERT for Chinese Few-shot NERCode0
Visual Prompt Tuning for Few-Shot Text Classification0
CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News MediaCode0
Eureka: Neural Insight Learning for Knowledge Graph Reasoning0
MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification0
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning0
Offline Handwritten Amharic Character Recognition Using Few-shot LearningCode0
Contrastive Graph Few-Shot Learning0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data0
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot LearningCode1
Revisiting Few-Shot Learning from a Causal PerspectiveCode0
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets0
Collaboration of Pre-trained Models Makes Better Few-shot Learner0
Learning Chess With Language Models and Transformers0
Efficient Few-Shot Learning Without PromptsCode4
A Few Shot Multi-Representation Approach for N-gram Spotting in Historical Manuscripts0
AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization0
Automatic Label Sequence Generation for Prompting Sequence-to-sequence ModelsCode1
Meta-Adapters: Parameter Efficient Few-shot Fine-tuning through Meta-LearningCode0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification0
Few-Shot Classification with Contrastive Learning0
Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registrationCode0
FETA: Towards Specializing Foundation Models for Expert Task ApplicationsCode1
Data-Driven Target Localization Using Adaptive Radar Processing and Convolutional Neural Networks0
A Study on Representation Transfer for Few-Shot Learning0
Class-Specific Channel Attention for Few-Shot Learning0
How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models0
Learn to Adapt to New Environment from Past Experience and Few Pilot0
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications0
Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks0
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results0
Automatic Identification of Coal and Rock/Gangue Based on DenseNet and Gaussian Process0
Prompt Tuning with Soft Context Sharing for Vision-Language ModelsCode0
Expanding continual few-shot learning benchmarks to include recognition of specific instancesCode0
Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEsCode0
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal PerspectiveCode1
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification0
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
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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