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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2401–2450 of 2964 papers

TitleStatusHype
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization—0
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning—0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model—0
FontTransformer: Few-shot High-resolution Chinese Glyph Image Synthesis via Stacked Transformers—0
FORML: A Riemannian Hessian-free Method for Meta-learning on Stiefel Manifolds—0
Four Eyes Are Better Than Two: Harnessing the Collaborative Potential of Large Models via Differentiated Thinking and Complementary Ensembles—0
Free-HeadGAN: Neural Talking Head Synthesis with Explicit Gaze Control—0
Frequency Guidance Matters in Few-Shot Learning—0
FrLove : Could a Frenchman rapidly identify Lovecraft?—0
From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning—0
From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models—0
From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems—0
From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs—0
From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning—0
From User Preferences to Optimization Constraints Using Large Language Models—0
A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios—0
FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography—0
FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing—0
FS-SS: Few-Shot Learning for Fast and Accurate Spike Sorting of High-channel Count Probes—0
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners—0
Function Contrastive Learning of Transferable Representations—0
Function Contrastive Learning of Transferable Meta-Representations—0
Function-words Enhanced Attention Networks for Few-Shot Inverse Relation Classification—0
FungiTastic: A multi-modal dataset and benchmark for image categorization—0
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning—0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
Gaussian Process Conditional Density Estimation—0
GCCN: Global Context Convolutional Network—0
GDC- Generalized Distribution Calibration for Few-Shot Learning—0
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
Generalization Bounds for Few-Shot Transfer Learning with Pretrained Classifiers—0
Generalization of Fitness Exercise Recognition from Doppler Measurements by Domain-adaption and Few-Shot Learning—0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Generalized Reinforcement Meta Learning for Few-Shot Optimization—0
Generalized Sampling Method for Few Shot Learning—0
Generalized Zero and Few-Shot Transfer for Facial Forgery Detection—0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—0
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation—0
Generating Pseudo-labels Adaptively for Few-shot Model-Agnostic Meta-Learning—0
Generating Synthetic Datasets for Few-shot Prompt Tuning—0
Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification—0
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation—0
Generative Pre-trained Autoregressive Diffusion Transformer—0
Geometric Mean Improves Loss For Few-Shot Learning—0
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts—0
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task—0
GLAD: Generalizable Tuning for Vision-Language Models—0
Global in Local: A Convolutional Transformer for SAR ATR FSL—0
Gpachov at CheckThat! 2023: A Diverse Multi-Approach Ensemble for Subjectivity Detection in News Articles—0
Show:102550
← PrevPage 49 of 60Next →
All datasetsMedConceptsQADTDFGVC-AircraftMini-ImageNet - 5-Shot LearningMini-Imagenet 5-way (1-shot)Stanford CarsMini-ImageNet - 1-Shot LearningPubMedQACaltech101CaseHOLDEuroSATFlowers-102

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified