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 751–775 of 2964 papers

Title	Date	Tasks	Status	Score
AffinityNet: semi-supervised few-shot learning for disease type prediction	May 22, 2018	Deep LearningFew-Shot Learning	CodeCode Available	5
Closed-Form Feedback-Free Learning with Forward Projection	Jan 27, 2025	Few-Shot LearningForm	CodeCode Available	5
L-HYDRA: Multi-Head Physics-Informed Neural Networks	Jan 5, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	5
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning	Apr 18, 2021	Continual LearningFew-Shot Learning	CodeCode Available	5
Limited Data Rolling Bearing Fault Diagnosis with Few-shot Learning	Aug 22, 2019	Fault DiagnosisFew-Shot Learning	CodeCode Available	5
CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News Media	Oct 1, 2022	ArticlesContrastive Learning	CodeCode Available	5
Probing Predictions on OOD Images via Nearest Categories	Nov 17, 2020	Adversarial RobustnessFew-Shot Learning	CodeCode Available	5
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning	Dec 1, 2018	Few-Shot LearningMetric Learning	CodeCode Available	5
Are LSTMs Good Few-Shot Learners?	Oct 22, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	5
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning	Dec 18, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	5
Are Large Language Models Robust Coreference Resolvers?	May 23, 2023	coreference-resolutionCoreference Resolution	CodeCode Available	5
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint	Oct 6, 2021	Few-Shot LearningTrajectory Prediction	CodeCode Available	5
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding	Jun 9, 2023	Few-Shot Learningimage-classification	CodeCode Available	5
ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?	Nov 28, 2023	Few-Shot LearningInformation Retrieval	CodeCode Available	5
Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution	Feb 23, 2021	Few-Shot LearningLearning Theory	CodeCode Available	5
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning	May 25, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	5
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationships	Oct 27, 2022	Domain AdaptationFew-Shot Learning	CodeCode Available	5
Learning to Propagate for Graph Meta-Learning	Sep 11, 2019	Dataset GenerationFew-Shot Image Classification	CodeCode Available	5
Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object Detection	Jul 23, 2020	Few-Shot LearningFew-Shot Object Detection	CodeCode Available	5
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning	May 15, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	5
Learning to learn via Self-Critique	May 24, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	5
Learning to Learn By Self-Critique	Dec 1, 2019	Few-Shot LearningMeta-Learning	CodeCode Available	5
Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language	Sep 30, 2024	Few-Shot LearningSentence	CodeCode Available	5
APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World Agents	Nov 26, 2024	Few-Shot LearningLarge Language Model	CodeCode Available	5
Class-Agnostic Counting	Nov 1, 2018	Crowd CountingFew-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