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 1726–1750 of 2964 papers

Title	Date	Tasks	Status
JASMINE: Arabic GPT Models for Few-Shot Learning	Dec 21, 2022	Few-Shot Learning	—Unverified
OpineSum: Entailment-based self-training for abstractive opinion summarization	Dec 21, 2022	Abstractive Text SummarizationArticles	—Unverified
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning	Dec 21, 2022	Few-Shot LearningLanguage Modelling	—Unverified
In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models	Dec 20, 2022	Few-Shot LearningIn-Context Learning	—Unverified
Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class	Dec 19, 2022	Document ClassificationFew-Shot Learning	—Unverified
A Comparative Study on Textual Saliency of Styles from Eye Tracking, Annotations, and Language Models	Dec 19, 2022	Few-Shot Learning	CodeCode Available
Check-worthy Claim Detection across Topics for Automated Fact-checking	Dec 16, 2022	Data AugmentationFact Checking	—Unverified
ALERT: Adapting Language Models to Reasoning Tasks	Dec 16, 2022	Few-Shot LearningLanguage Modeling	—Unverified
FewFedWeight: Few-shot Federated Learning Framework across Multiple NLP Tasks	Dec 16, 2022	Federated LearningFew-Shot Learning	—Unverified
Multi-task Learning for Cross-Lingual Sentiment Analysis	Dec 14, 2022	ArticlesFew-Shot Learning	CodeCode Available
A Statistical Model for Predicting Generalization in Few-Shot Classification	Dec 13, 2022	Few-Shot Learning	CodeCode Available
Localized Latent Updates for Fine-Tuning Vision-Language Models	Dec 13, 2022	Few-Shot Learning	—Unverified
Technical Report -- Competition Solution for Prompt Tuning using Pretrained Language Model	Dec 13, 2022	Few-Shot LearningLanguage Modeling	—Unverified
Cap2Aug: Caption guided Image to Image data Augmentation	Dec 11, 2022	ClassificationCross-Domain Few-Shot	—Unverified
An AI-Powered VVPAT Counter for Elections in India	Dec 9, 2022	Few-Shot LearningImage Classification	CodeCode Available
Demystifying Prompts in Language Models via Perplexity Estimation	Dec 8, 2022	Few-Shot Learning	—Unverified
Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology	Dec 7, 2022	Few-Shot Learning	—Unverified
Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning	Dec 7, 2022	Active LearningEfficient Exploration	—Unverified
Multi-Objective Linear Ensembles for Robust and Sparse Training of Few-Bit Neural Networks	Dec 7, 2022	Combinatorial OptimizationFew-Shot Learning	CodeCode Available
JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset	Dec 7, 2022	Cross-Lingual TransferFew-Shot Learning	—Unverified
Few-shot Medical Image Segmentation with Cycle-resemblance Attention	Dec 7, 2022	Few-Shot LearningImage Segmentation	—Unverified
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration	Dec 5, 2022	Few-Shot LearningFew-Shot Text Classification	—Unverified
Cross-Domain Few-Shot Relation Extraction via Representation Learning and Domain Adaptation	Dec 5, 2022	Contrastive LearningCross-Domain Few-Shot	—Unverified
Can In-context Learners Learn a Reasoning Concept from Demonstrations?	Dec 3, 2022	Few-Shot LearningIn-Context Learning	CodeCode Available
Few-Shot Nested Named Entity Recognition	Dec 2, 2022	Contrastive LearningFew-Shot Learning	—Unverified

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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