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 1601–1650 of 2964 papers

TitleStatusHype
UniPredict: Large Language Models are Universal Tabular Classifiers—0
Unknown Presentation Attack Detection against Rational Attackers—0
Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation—0
Unleashing the Potential of CNNs for Interpretable Few-Shot Learning—0
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps—0
Unsupervised Few Shot Learning via Self-supervised Training—0
Unsupervised Few-shot Learning via Self-supervised Training—0
Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code—0
Unsupervised Meta-Learning For Few-Shot Image Classification—0
Unsupervised Task Design to Meta-Train Medical Image Classifiers—0
Unsupervised Transfer Learning with Self-Supervised Remedy—0
UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.—0
Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development—0
User Behavior Analysis in Privacy Protection with Large Language Models: A Study on Privacy Preferences with Limited Data—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity—0
Using dependency parsing for few-shot learning in distributional semantics—0
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic—0
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures—0
Using Grammar Masking to Ensure Syntactic Validity in LLM-based Modeling Tasks—0
Using Guided Transfer Learning to Predispose AI Agent to Learn Efficiently from Small RNA-sequencing Datasets—0
Using Large Language Models for the Interpretation of Building Regulations—0
Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level—0
Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events—0
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES—0
ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning—0
Variadic Learning by Bayesian Nonparametric Deep Embedding—0
Variational Few-Shot Learning—0
Variational Neuron Shifting for Few-Shot Image Classification Across Domains—0
Variational Supervised Contrastive Learning—0
VesselShot: Few-shot learning for cerebral blood vessel segmentation—0
VIABLE: Fast Adaptation via Backpropagating Learned Loss—0
Video to Video Generative Adversarial Network for Few-shot Learning Based on Policy Gradient—0
ViRefSAM: Visual Reference-Guided Segment Anything Model for Remote Sensing Segmentation—0
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models—0
Visually grounded few-shot word learning in low-resource settings—0
Visually Grounded Speech Models for Low-resource Languages and Cognitive Modelling—0
Visual Prompt Tuning for Few-Shot Text Classification—0
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction—0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification—0
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery—0
VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models—0
Vulnerability of LLMs to Vertically Aligned Text Manipulations—0
WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch—0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning—0
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition—0
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings—0
Weakly-Supervised Surgical Phase Recognition—0
What does the Failure to Reason with "Respectively" in Zero/Few-Shot Settings Tell Us about Language Models?—0
Show:102550
← PrevPage 33 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