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 1976–2000 of 2964 papers

Title	Date	Tasks	Status
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation	Dec 28, 2020	Few-Shot LearningPoint Cloud Segmentation	—Unverified
Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMs	Jan 23, 2025	DiagnosticFew-Shot Learning	—Unverified
Compressor-Based Classification for Atrial Fibrillation Detection	Aug 25, 2023	Atrial Fibrillation DetectionAvg	—Unverified
Concept Discovery for Fast Adapatation	Jan 19, 2023	Few-Shot LearningMeta-Learning	—Unverified
Conditional Neural Processes for Molecules	Oct 17, 2022	Bayesian OptimizationBenchmarking	—Unverified
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning	Sep 29, 2021	Contrastive LearningCross-Domain Few-Shot	—Unverified
Configuration Validation with Large Language Models	Oct 15, 2023	Code GenerationFew-Shot Learning	—Unverified
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis	Mar 19, 2025	Few-Shot LearningImage Generation	—Unverified
ConML: A Universal Meta-Learning Framework with Task-Level Contrastive Learning	Oct 8, 2024	Contrastive LearningFew-Shot Learning	—Unverified
ConQX: Semantic Expansion of Spoken Queries for Intent Detection based on Conditioned Text Generation	Sep 2, 2021	Few-Shot LearningIntent Detection	—Unverified
Conservative Generator, Progressive Discriminator: Coordination of Adversaries in Few-shot Incremental Image Synthesis	Jul 29, 2022	Few-Shot LearningImage Generation	—Unverified
Consistency Analysis of ChatGPT	Mar 11, 2023	Few-Shot LearningLanguage Modelling	—Unverified
ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning	Nov 8, 2022	Contrastive LearningFew-Shot Learning	—Unverified
Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding	May 25, 2019	Few-Shot LearningMetric Learning	—Unverified
Constrained Few-Shot Learning: Human-Like Low Sample Complexity Learning and Non-Episodic Text Classification	Aug 17, 2022	Few-Shot LearningMeta-Learning	—Unverified
Context-Agnostic Learning Using Synthetic Data	Jan 1, 2021	ClassificationFew-Shot Learning	—Unverified
Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods	Oct 22, 2024	Few-Shot LearningIn-Context Learning	—Unverified
Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity	Oct 11, 2024	Few-Shot Learning	—Unverified
Contextual HyperNetworks for Novel Feature Adaptation	Apr 12, 2021	Few-Shot LearningImputation	—Unverified
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery	Oct 28, 2024	Drug DiscoveryFew-Shot Learning	—Unverified
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning	Jan 11, 2023	class-incremental learningClass Incremental Learning	—Unverified
Continual Few-Shot Learning with Adversarial Class Storage	Jul 10, 2022	continual few-shot learningFew-Shot Learning	—Unverified
Continual Local Replacement for Few-shot Learning	Jan 23, 2020	Few-Shot LearningGeneral Classification	—Unverified
Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning	Mar 7, 2024	class-incremental learningClass Incremental Learning	—Unverified
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning	Dec 21, 2022	Few-Shot LearningLanguage Modelling	—Unverified

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All datasets MedConceptsQA DTD FGVC-Aircraft Mini-ImageNet - 5-Shot Learning Mini-Imagenet 5-way (1-shot)Stanford Cars Mini-ImageNet - 1-Shot Learning PubMedQA Caltech101 CaseHOLD EuroSAT Flowers-102

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	gpt-4-0125-preview	Accuracy	61.91	—	Unverified
2	gpt-4-0125-preview	Accuracy	52.49	—	Unverified
3	gpt-3.5-turbo	Accuracy	41.48	—	Unverified
4	gpt-3.5-turbo	Accuracy	37.06	—	Unverified
5	johnsnowlabs/JSL-MedMNX-7B	Accuracy	25.63	—	Unverified
6	yikuan8/Clinical-Longformer	Accuracy	25.55	—	Unverified
7	BioMistral/BioMistral-7B-DARE	Accuracy	25.06	—	Unverified
8	yikuan8/Clinical-Longformer	Accuracy	25.04	—	Unverified
9	PharMolix/BioMedGPT-LM-7B	Accuracy	24.92	—	Unverified
10	PharMolix/BioMedGPT-LM-7B	Accuracy	24.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	67.27	—	Unverified
2	SaSPA + CAL	4-shot Accuracy	48.3	—	Unverified
3	Real-Guidance + CAL	4-shot Accuracy	41.5	—	Unverified
4	CAL	4-shot Accuracy	40.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SaSPA + CAL	Harmonic mean	52.2	—	Unverified
2	CAL	Harmonic mean	35.2	—	Unverified
3	Variational Prompt Tuning	Harmonic mean	34.69	—	Unverified
4	Real-Guidance + CAL	Harmonic mean	34.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BGNN	Accuracy	92.7	—	Unverified
2	TIM-GD	Accuracy	87.4	—	Unverified
3	UNEM-Gaussian	Accuracy	66.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	EASY (transductive)	Accuracy	82.75	—	Unverified
2	HCTransformers	5 way 1~2 shot	74.74	—	Unverified
3	HyperShot	Accuracy	53.18	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SaSPA + CAL	4-shot Accuracy	66.7	—	Unverified
2	Real-Guidance + CAL	4-shot Accuracy	44.3	—	Unverified
3	CAL	4-shot Accuracy	42.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HCTransformers	Acc	74.74	—	Unverified
2	DPGN	Acc	67.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MetaGen Blended RAG (zero-shot)	Accuracy	77.9	—	Unverified
2	CoT-T5-11B (1024 Shot)	Accuracy	73.42	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	96.44	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CoT-T5-11B (1024 Shot)	Accuracy	68.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	77.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	81.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	91.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CovidExpert	AUC-ROC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CoT-T5-11B (1024 Shot)	Accuracy	78.02	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	65.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	73.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	96.82	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	73.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	78.51	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UNEM-Gaussian	Accuracy	52.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Variational Prompt Tuning	Harmonic mean	79	—	Unverified