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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 1026–1050 of 2964 papers

Title	Date	Tasks	Status	Score
Interval Bound Interpolation for Few-shot Learning with Few Tasks	Apr 7, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	5
Interactive Symbol Grounding with Complex Referential Expressions	Jul 1, 2022	Few-Shot LearningNegation	CodeCode Available	5
IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model	Dec 10, 2024	ArticlesFew-Shot Learning	CodeCode Available	5
Meta Architecture Search	Dec 22, 2018	Bayesian InferenceFew-Shot Learning	CodeCode Available	5
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning	Jan 11, 2020	BIG-bench Machine LearningCausal Discovery	CodeCode Available	5
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models	May 4, 2023	Few-Shot LearningGraph Clustering	CodeCode Available	5
Instance-aware Prompt Learning for Language Understanding and Generation	Jan 18, 2022	Few-Shot LearningPrompt Learning	CodeCode Available	5
A Maximum Log-Likelihood Method for Imbalanced Few-Shot Learning Tasks	Nov 26, 2022	Few-Shot Learning	CodeCode Available	5
SIP: Injecting a Structural Inductive Bias into a Seq2Seq Model by Simulation	Oct 1, 2023	Few-Shot LearningInductive Bias	CodeCode Available	5
Instance-level Few-shot Learning with Class Hierarchy Mining	Apr 15, 2023	Few-shot Instance SegmentationFew-Shot Learning	CodeCode Available	5
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning	Nov 24, 2023	Few-Shot Learning	CodeCode Available	5
Deep Content Understanding Toward Entity and Aspect Target Sentiment Analysis on Foundation Models	Jul 4, 2024	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	5
RelationNet2: Deep Comparison Columns for Few-Shot Learning	Nov 17, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	5
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning	Jul 14, 2022	BIG-bench Machine LearningFew-Shot Learning	CodeCode Available	5
A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education	Jan 24, 2025	Few-Shot LearningLanguage Modeling	CodeCode Available	5
Decoder Choice Network for Meta-Learning	Sep 25, 2019	DecoderEnsemble Learning	CodeCode Available	5
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation	Mar 25, 2023	Federated LearningFew-Shot Learning	CodeCode Available	5
In-context Learning and Gradient Descent Revisited	Nov 13, 2023	Few-Shot LearningIn-Context Learning	CodeCode Available	5
Incremental Few-Shot Learning with Attention Attractor Networks	Oct 16, 2018	Few-Shot LearningGeneral Classification	CodeCode Available	5
Kajal: Extracting Grammar of a Source Code Using Large Language Models	Dec 12, 2024	Few-Shot LearningPrompt Engineering	CodeCode Available	5
Learning Deep Disentangled Embeddings with the F-Statistic Loss	Feb 14, 2018	Few-Shot Learning	CodeCode Available	5
Dataset2Vec: Learning Dataset Meta-Features	May 27, 2019	Auxiliary LearningFew-Shot Learning	CodeCode Available	5
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification	Aug 22, 2019	Few-Shot LearningFew-Shot Text Classification	CodeCode Available	5
Data-Efficient Language Shaped Few-shot Image Classification	Nov 1, 2021	ClassificationFew-Shot Image Classification	CodeCode Available	5
Adaptive Prototypical Networks	Nov 22, 2022	Few-Shot Learning	CodeCode Available	5

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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