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 426–450 of 2964 papers

Title	Date	Tasks	Status	Hype	Score
Concept Learners for Few-Shot Learning	Jul 14, 2020	Few-Shot LearningFine-Grained Image Classification	CodeCode Available	1	5
Overcoming challenges in leveraging GANs for few-shot data augmentation	Mar 30, 2022	ClassificationData Augmentation	CodeCode Available	1	5
Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data	May 30, 2025	Continual LearningFew-Shot Learning	CodeCode Available	1	5
Channel Importance Matters in Few-Shot Image Classification	Jun 16, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
Federated Few-shot Learning	Jun 17, 2023	ArticlesFederated Learning	CodeCode Available	1	5
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning	Aug 13, 2021	Data AugmentationFew-Shot Learning	CodeCode Available	1	5
Charting the Right Manifold: Manifold Mixup for Few-shot Learning	Jul 28, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark	Jul 15, 2021	Few-Shot LearningMachine Reading Comprehension	CodeCode Available	1	5
Few-Shot and Continual Learning with Attentive Independent Mechanisms	Jul 29, 2021	Continual LearningFew-Shot Learning	CodeCode Available	1	5
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning	Mar 1, 2024	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1	5
Few-shot Adaptation Works with UnpredicTable Data	Aug 1, 2022	Domain AdaptationFew-Shot Learning	CodeCode Available	1	5
MAML is a Noisy Contrastive Learner in Classification	Jun 29, 2021	ClassificationFew-Shot Learning	CodeCode Available	1	5
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy	Jun 28, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1	5
FLEX: Unifying Evaluation for Few-Shot NLP	Jul 15, 2021	Few-Shot LearningLanguage Modeling	CodeCode Available	1	5
FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification	Nov 22, 2024	DiagnosticFew-Shot Learning	CodeCode Available	1	5
Few-shot bioacoustic event detection at the DCASE 2023 challenge	Jun 15, 2023	Event DetectionFew-Shot Learning	CodeCode Available	1	5
Few-shot Classification via Adaptive Attention	Aug 6, 2020	ClassificationFew-Shot Learning	CodeCode Available	1	5
Finetune like you pretrain: Improved finetuning of zero-shot vision models	Dec 1, 2022	DescriptiveFew-Shot Learning	CodeCode Available	1	5
Match Them Up: Visually Explainable Few-shot Image Classification	Nov 25, 2020	ClassificationFew-Shot Image Classification	CodeCode Available	1	5
Few-Shot Bot: Prompt-Based Learning for Dialogue Systems	Oct 15, 2021	ChatbotDialogue State Tracking	CodeCode Available	1	5
Class-Incremental Domain Adaptation with Smoothing and Calibration for Surgical Report Generation	Jul 23, 2021	Domain AdaptationFew-Shot Learning	CodeCode Available	1	5
A Rationale-Centric Framework for Human-in-the-loop Machine Learning	Mar 24, 2022	BIG-bench Machine LearningFew-Shot Learning	CodeCode Available	1	5
Few-Shot Diffusion Models	May 30, 2022	DenoisingFew-Shot Learning	CodeCode Available	1	5
A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition	Sep 26, 2020	Few-Shot LearningFew-Shot Object Detection	CodeCode Available	1	5
Few-Shot Classification with Feature Map Reconstruction Networks	Dec 2, 2020	ClassificationCross-Domain Few-Shot	CodeCode Available	1	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