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 976–1000 of 2964 papers

Title	Date	Tasks	Status	Score
Towards Context-Agnostic Learning Using Synthetic Data	May 29, 2020	Few-Shot Learningimage-classification	CodeCode Available	5
L-HYDRA: Multi-Head Physics-Informed Neural Networks	Jan 5, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	5
Kernel Relative-prototype Spectral Filtering for Few-shot Learning	Jul 24, 2022	Few-Shot Learning	CodeCode Available	5
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks	Jun 9, 2019	ColorizationFew-Shot Learning	CodeCode Available	5
Few-Shot Classification of Skin Lesions from Dermoscopic Images by Meta-Learning Representative Embeddings	Oct 30, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	5
ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production Code	Nov 25, 2024	Few-Shot LearningPrompt Engineering	CodeCode Available	5
Preserving Fine-Grain Feature Information in Classification via Entropic Regularization	Aug 7, 2022	ClassificationDiversity	CodeCode Available	5
Beyond Textual Constraints: Learning Novel Diffusion Conditions with Fewer Examples	Jan 1, 2024	Few-Shot Learning	CodeCode Available	5
Kajal: Extracting Grammar of a Source Code Using Large Language Models	Dec 12, 2024	Few-Shot LearningPrompt Engineering	CodeCode Available	5
Dictionary-Assisted Supervised Contrastive Learning	Oct 27, 2022	Contrastive LearningData Augmentation	CodeCode Available	5
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback	Sep 30, 2024	Few-Shot LearningPrompt Engineering	CodeCode Available	5
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models	May 4, 2023	Few-Shot LearningGraph Clustering	CodeCode Available	5
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction	Aug 16, 2023	AttributeAttribute Value Extraction	CodeCode Available	5
An AI-Powered VVPAT Counter for Elections in India	Dec 9, 2022	Few-Shot LearningImage Classification	CodeCode Available	5
Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI	Oct 12, 2021	Few-Shot LearningFew-Shot NLI	CodeCode Available	5
Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution	May 9, 2024	Few-Shot LearningNatural Language Inference	CodeCode Available	5
Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness	Oct 27, 2024	Domain GeneralizationFew-Shot Learning	CodeCode Available	5
Interval Bound Interpolation for Few-shot Learning with Few Tasks	Apr 7, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	5
Do prompt positions really matter?	May 23, 2023	Few-Shot LearningNatural Language Understanding	CodeCode Available	5
Knowledge Graph Transfer Network for Few-Shot Recognition	Nov 21, 2019	Few-Shot Image ClassificationFew-Shot Learning	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
Descriptor and Word Soups: Overcoming the Parameter Efficiency Accuracy Tradeoff for Out-of-Distribution Few-shot Learning	Nov 21, 2023	Domain GeneralizationFew-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
Instance-level Few-shot Learning with Class Hierarchy Mining	Apr 15, 2023	Few-shot Instance SegmentationFew-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

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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