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 401450 of 2964 papers

TitleStatusHype
animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacousticsCode1
Calibrate Before Use: Improving Few-Shot Performance of Language ModelsCode1
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Iterative label cleaning for transductive and semi-supervised few-shot learningCode1
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot ClassificationCode1
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Effect of Pre-Training Scale on Intra- and Inter-Domain Full and Few-Shot Transfer Learning for Natural and Medical X-Ray Chest ImagesCode1
Easter2.0: Improving convolutional models for handwritten text recognitionCode1
Label Semantics for Few Shot Named Entity RecognitionCode1
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot LearningCode1
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple IngredientsCode1
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With SupervoxelsCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Language Quantized AutoEncoders: Towards Unsupervised Text-Image AlignmentCode1
Efficient Few-shot Learning for Multi-label Classification of Scientific Documents with Many ClassesCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular LearningCode1
CDFSL-V: Cross-Domain Few-Shot Learning for VideosCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient RepresentationsCode1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft PromptsCode1
An Overview of Deep Learning Architectures in Few-Shot Learning DomainCode1
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic SegmentationCode1
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic SegmentationCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
Overcoming challenges in leveraging GANs for few-shot data augmentationCode1
Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential DataCode1
Channel Importance Matters in Few-Shot Image ClassificationCode1
Learning Opinion Summarizers by Selecting Informative ReviewsCode1
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image ClassificationCode1
Charting the Right Manifold: Manifold Mixup for Few-shot LearningCode1
Towards Task Sampler Learning for Meta-LearningCode1
Learning to Segment the TailCode1
'Less Than One'-Shot Learning: Learning N Classes From M<N SamplesCode1
Leveraging Hierarchical Structures for Few-Shot Musical Instrument RecognitionCode1
LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5Code1
ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical NotesCode1
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
BOIL: Towards Representation Change for Few-shot LearningCode1
LLMeBench: A Flexible Framework for Accelerating LLMs BenchmarkingCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Domain-Adaptive Few-Shot LearningCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report GenerationCode1
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal PerspectiveCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
A Few-shot Learning Approach for Historical Ciphered Manuscript RecognitionCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
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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