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 276–300 of 2964 papers

Title	Date	Tasks	Status	Hype	Score
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network	Jan 5, 2020	Face SwappingFew-Shot Learning	CodeCode Available	1	5
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining	Feb 15, 2024	AllFew-Shot Learning	CodeCode Available	1	5
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid	Oct 30, 2022	Few-Shot LearningSelection bias	CodeCode Available	1	5
Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences	Jan 27, 2022	Clinical KnowledgeDocument Classification	CodeCode Available	1	5
Feature Generation for Long-tail Classification	Nov 10, 2021	ClassificationFew-Shot Learning	CodeCode Available	1	5
FETA: Towards Specializing Foundation Models for Expert Task Applications	Sep 8, 2022	Domain GeneralizationFew-Shot Learning	CodeCode Available	1	5
Extending Context Window of Large Language Models via Semantic Compression	Dec 15, 2023	Few-Shot LearningInformation Retrieval	CodeCode Available	1	5
Exploring Foundation Models Fine-Tuning for Cytology Classification	Nov 22, 2024	ClassificationFew-Shot Learning	CodeCode Available	1	5
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs	May 5, 2022	ClassificationFew-Shot Learning	CodeCode Available	1	5
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning	Mar 1, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
Explanation-Guided Training for Cross-Domain Few-Shot Classification	Jul 17, 2020	ClassificationCross-Domain Few-Shot	CodeCode Available	1	5
Exploring Efficient Few-shot Adaptation for Vision Transformers	Jan 6, 2023	Few-Shot Learning	CodeCode Available	1	5
FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter	Mar 31, 2021	Few-shot Instance SegmentationFew-Shot Learning	CodeCode Available	1	5
EventCLIP: Adapting CLIP for Event-based Object Recognition	Jun 10, 2023	Few-Shot LearningObject	CodeCode Available	1	5
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization	Jun 19, 2021	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1	5
Can Explanations Be Useful for Calibrating Black Box Models?	Oct 14, 2021	Extractive Question-AnsweringFew-Shot Learning	CodeCode Available	1	5
CDFSL-V: Cross-Domain Few-Shot Learning for Videos	Sep 7, 2023	Action RecognitionCross-Domain Few-Shot	CodeCode Available	1	5
Example-Based Named Entity Recognition	Aug 24, 2020	Few-Shot Learningnamed-entity-recognition	CodeCode Available	1	5
3D-IDS: Doubly Disentangled Dynamic Intrusion Detection	Jul 2, 2023	DisentanglementFew-Shot Learning	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
Overcoming challenges in leveraging GANs for few-shot data augmentation	Mar 30, 2022	ClassificationData Augmentation	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
Charting the Right Manifold: Manifold Mixup for Few-shot Learning	Jul 28, 2019	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition	Oct 18, 2023	Audio ClassificationContrastive Learning	CodeCode Available	1	5
Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification	Jan 1, 2023	Few-Shot Image ClassificationFew-Shot Learning	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