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 1201–1225 of 2964 papers

Title	Date	Tasks	Status	Score
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing	Feb 25, 2019	Cross-lingual zero-shot dependency parsingDependency Parsing	CodeCode Available	5
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization	May 3, 2023	Abstractive Text SummarizationContrastive Learning	CodeCode Available	5
Few-shot learning through contextual data augmentation	Mar 31, 2021	Data AugmentationFew-Shot Learning	CodeCode Available	5
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding	Jun 9, 2023	Few-Shot Learningimage-classification	CodeCode Available	5
Contextualizing Enhances Gradient Based Meta Learning	Jul 17, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	5
Contextual Interaction via Primitive-based Adversarial Training For Compositional Zero-shot Learning	Jun 21, 2024	AttributeCompositional Zero-Shot Learning	CodeCode Available	5
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation	Oct 18, 2023	FairnessFew-Shot Learning	CodeCode Available	5
HQP: A Human-Annotated Dataset for Detecting Online Propaganda	Apr 28, 2023	Few-Shot LearningPropaganda detection	CodeCode Available	5
High-order structure preserving graph neural network for few-shot learning	May 29, 2020	Few-Shot LearningGraph Neural Network	CodeCode Available	5
Contextual Gradient Scaling for Few-Shot Learning	Oct 20, 2021	Cross-Domain Few-ShotFew-Shot Learning	CodeCode Available	5
Hierarchical Variational Memory for Few-shot Learning Across Domains	Dec 15, 2021	Few-Shot LearningVariational Inference	CodeCode Available	5
Karyotype AI for Precision Oncology	Nov 20, 2022	Few-Shot LearningInductive Bias	CodeCode Available	5
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction	Oct 1, 2023	Few-Shot LearningGraph Neural Network	CodeCode Available	5
FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech	May 25, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	5
Hierarchy-based Image Embeddings for Semantic Image Retrieval	Sep 26, 2018	Few-Shot LearningImage Retrieval	CodeCode Available	5
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study	Jan 14, 2024	Deep LearningDepression Detection	CodeCode Available	5
GSHOT: Few-shot Generative Modeling of Labeled Graphs	Jun 6, 2023	Drug DiscoveryFew-Shot Learning	CodeCode Available	5
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction	Feb 4, 2023	Few-Shot LearningMolecular Docking	CodeCode Available	5
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction	Aug 20, 2022	DenoisingFew-Shot Learning	CodeCode Available	5
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction	Feb 17, 2024	Few-Shot LearningLanguage Modelling	CodeCode Available	5
Grid Cell Path Integration For Movement-Based Visual Object Recognition	Feb 17, 2021	Few-Shot LearningNavigate	CodeCode Available	5
Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs	Feb 7, 2023	Few-Shot Learning	CodeCode Available	5
Graph convolutional networks for learning with few clean and many noisy labels	Oct 1, 2019	Few-Shot LearningGeneral Classification	CodeCode Available	5
Graph Few-shot Learning via Knowledge Transfer	Oct 7, 2019	Few-Shot LearningGeneral Classification	CodeCode Available	5
Graph Few-shot Learning with Task-specific Structures	Oct 21, 2022	ClassificationFew-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