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

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Showing 2651–2700 of 2964 papers

TitleStatusHype
Learning to Learn Dense Gaussian Processes for Few-Shot Learning—0
Learning to learn generative programs with Memoised Wake-Sleep—0
Learning to Learn Recognising Biomedical Entities from Multiple Domains with Task Hardness—0
Learning to Learn Semantic Factors in Heterogeneous Image Classification—0
Learning-to-Learn the Wave Angle Estimation—0
Learning to Learn Weight Generation via Local Consistency Diffusion—0
Learning to Learn with Conditional Class Dependencies—0
Learning to Learn with Indispensable Connections—0
Learning to Learn with Smooth Regularization—0
Learning to Profile: User Meta-Profile Network for Few-Shot Learning—0
Learning to Select Base Classes for Few-shot Classification—0
Learning to Skip for Language Modeling—0
Learn to Adapt to New Environment from Past Experience and Few Pilot—0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning—0
Learn To Learn More Precisely—0
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video—0
Less is More: A Closer Look at Semantic-based Few-Shot Learning—0
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Less than Few: Self-Shot Video Instance Segmentation—0
Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning—0
Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation—0
Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation—0
Text Descriptions are Compressive and Invariant Representations for Visual Learning—0
Leveraging pre-trained language models for conversational information seeking from text—0
Leveraging Twitter Data for Sentiment Analysis of Transit User Feedback: An NLP Framework—0
Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs—0
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
LFD-ProtoNet: Prototypical Network Based on Local Fisher Discriminant Analysis for Few-shot Learning—0
Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition—0
Lifelong Wandering: A realistic few-shot online continual learning setting—0
Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification—0
Like Humans to Few-Shot Learning through Knowledge Permeation of Vision and Text—0
Limits of an AI program for solving college math problems—0
Linear algebra with transformers—0
Interpretable Few-Shot Learning via Linear Distillation—0
Livewired Neural Networks: Making Neurons That Fire Together Wire Together—0
LLM-augmented Preference Learning from Natural Language—0
LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations—0
LLM Distillation for Efficient Few-Shot Multiple Choice Question Answering—0
LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputation—0
Local descriptor-based multi-prototype network for few-shot Learning—0
Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning—0
Localized Latent Updates for Fine-Tuning Vision-Language Models—0
Local Propagation for Few-Shot Learning—0
Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction—0
LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models—0
Logos as a Well-Tempered Pre-train for Sign Language Recognition—0
Data-Efficient Pretraining via Contrastive Self-Supervision—0
Looking back to lower-level information in few-shot learning—0
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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