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 22512300 of 2964 papers

TitleStatusHype
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification0
ViTNF: Leveraging Neural Fields to Boost Vision Transformers in Generalized Category Discovery0
VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models0
Vulnerability of LLMs to Vertically Aligned Text Manipulations0
WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning0
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition0
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings0
Weakly-Supervised Surgical Phase Recognition0
What does the Failure to Reason with "Respectively" in Zero/Few-Shot Settings Tell Us about Language Models?0
What Makes Data-to-Text Generation Hard for Pretrained Language Models?0
What Makes Good Few-shot Examples for Vision-Language Models?0
What's in a Measurement? Using GPT-3 on SemEval 2021 Task 8 -- MeasEval0
When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework0
When hard negative sampling meets supervised contrastive learning0
When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning0
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects0
Which images to label for few-shot medical landmark detection?0
Will Multi-modal Data Improves Few-shot Learning?0
WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models for Lay Summarization of Scientific Articles0
WisPerMed at "Discharge Me!": Advancing Text Generation in Healthcare with Large Language Models, Dynamic Expert Selection, and Priming Techniques on MIMIC-IV0
Wordcraft: a Human-AI Collaborative Editor for Story Writing0
xCoT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning0
Zero and Few-shot Learning for Author Profiling0
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning0
Zero- and Few-Shot NLP with Pretrained Language Models0
Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer0
Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking0
Zero-Shot Cross-Lingual Document-Level Event Causality Identification with Heterogeneous Graph Contrastive Transfer Learning0
Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review0
Zero-Shot Learning via Class-Conditioned Deep Generative Models0
Learning to Select Base Classes for Few-shot Classification0
Learning to Skip for Language Modeling0
Learn to Adapt to New Environment from Past Experience and Few Pilot0
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning0
Learn To Learn More Precisely0
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video0
Less is More: A Closer Look at Semantic-based Few-Shot Learning0
Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification0
Less than Few: Self-Shot Video Instance Segmentation0
Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning0
Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation0
Wisdom of Instruction-Tuned Language Model Crowds. Exploring Model Label Variation0
Text Descriptions are Compressive and Invariant Representations for Visual Learning0
Leveraging pre-trained language models for conversational information seeking from text0
Leveraging Twitter Data for Sentiment Analysis of Transit User Feedback: An NLP Framework0
Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs0
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models0
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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