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 551–575 of 2964 papers

Title	Date	Tasks	Status	Hype
Easter2.0: Improving convolutional models for handwritten text recognition	May 30, 2022	Data AugmentationFew-Shot Learning	CodeCode Available	1
Few-Shot Learning for Opinion Summarization	Apr 30, 2020	Few-Shot LearningInformativeness	CodeCode Available	1
Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images	Aug 7, 2020	Few-Shot Learning	CodeCode Available	1
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning	Feb 2, 2024	Few-Shot LearningGraph Representation Learning	CodeCode Available	1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation	Sep 18, 2023	Contrastive LearningData Augmentation	CodeCode Available	1
Contrastive Meta-Learning for Partially Observable Few-Shot Learning	Jan 30, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification	Jul 26, 2022	Few-Shot LearningHyperspectral Image Classification	CodeCode Available	1
Contrastive Prototypical Network with Wasserstein Confidence Penalty	Oct 21, 2022	Contrastive LearningFew-Shot Learning	CodeCode Available	1
Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection	Jul 16, 2024	Fake News DetectionFew-Shot Learning	CodeCode Available	1
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot Learning	Jan 1, 2022	Few-Shot Learning	CodeCode Available	1
Convolutional Bypasses Are Better Vision Transformer Adapters	Jul 14, 2022	Few-Shot LearningInductive Bias	CodeCode Available	1
ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts	May 24, 2022	Few-Shot LearningLanguage Modelling	CodeCode Available	1
Few Shot Medical Image Segmentation with Cross Attention Transformer	Mar 24, 2023	Few-Shot LearningImage Segmentation	CodeCode Available	1
Few-Shot Microscopy Image Cell Segmentation	Jun 29, 2020	Cell SegmentationFew-Shot Learning	CodeCode Available	1
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP	Apr 18, 2021	Few-Shot Learning	CodeCode Available	1
Few-shot Natural Language Generation for Task-Oriented Dialog	Feb 27, 2020	Data-to-Text GenerationFew-Shot Learning	CodeCode Available	1
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems	Jun 3, 2023	Few-Shot LearningKeyword Spotting	CodeCode Available	1
Few-shot Open-set Recognition by Transformation Consistency	Mar 2, 2021	Few-Shot LearningOpen Set Learning	CodeCode Available	1
Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning	Jun 17, 2024	Few-Shot LearningRetrieval	CodeCode Available	1
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients	Jan 24, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Cost-effective Instruction Learning for Pathology Vision and Language Analysis	Jul 25, 2024	Few-Shot LearningLanguage Modelling	CodeCode Available	1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification	Aug 19, 2021	Causal Inferencecounterfactual	CodeCode Available	1
Covariate Distribution Aware Meta-learning	Jul 6, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach	Feb 11, 2021	Data AugmentationDeep Learning	CodeCode Available	1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data	Jun 14, 2021	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1

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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