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 601–625 of 2964 papers

Title	Date	Tasks	Status	Hype
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot Learning	Jan 1, 2024	DenoisingFew-Shot Learning	CodeCode Available	1
How to train your MAML	Oct 22, 2018	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization	Jun 19, 2021	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
Human few-shot learning of compositional instructions	Jan 14, 2019	Few-Shot Learning	CodeCode Available	1
Hyperbolic Image Embeddings	Apr 3, 2019	Few-Shot LearningGeneral Classification	CodeCode Available	1
HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks	May 31, 2022	Few-Shot LearningMeta-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
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
IFSS-Net: Interactive Few-Shot Siamese Network for Faster Muscle Segmentation and Propagation in Volumetric Ultrasound	Nov 26, 2020	DecoderFew-Shot Learning	CodeCode Available	1
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages	Jan 27, 2022	Cross-Modal RetrievalFew-Shot Learning	CodeCode Available	1
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
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing	Jan 31, 2023	Anomaly DetectionContinual Learning	CodeCode Available	1
Extending Context Window of Large Language Models via Semantic Compression	Dec 15, 2023	Few-Shot LearningInformation Retrieval	CodeCode Available	1
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data	Feb 1, 2023	Few-Shot Learning	CodeCode Available	1
DenoiSeg: Joint Denoising and Segmentation	May 6, 2020	DenoisingFew-Shot Learning	CodeCode Available	1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs	Oct 15, 2023	Few-Shot LearningGraph Learning	CodeCode Available	1
In-Context Learning Demonstration Selection via Influence Analysis	Feb 19, 2024	Few-Shot LearningIn-Context Learning	CodeCode Available	1
In-Context Learning for Few-Shot Dialogue State Tracking	Mar 16, 2022	Dialogue State TrackingFew-Shot Learning	CodeCode Available	1
Information Maximization for Few-Shot Learning	Dec 1, 2020	Few-Shot LearningMeta-Learning	CodeCode Available	1
Instance Credibility Inference for Few-Shot Learning	Mar 26, 2020	Data AugmentationFew-Shot Image Classification	CodeCode Available	1
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis	Oct 12, 2022	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Integrative Few-Shot Learning for Classification and Segmentation	Mar 29, 2022	ClassificationFew-Shot Classification and Segmentation	CodeCode Available	1
Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models	Sep 20, 2022	Few-Shot LearningLanguage Modeling	CodeCode Available	1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning	Sep 11, 2023	Few-Shot Learningparameter-efficient fine-tuning	CodeCode Available	1
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models	Jun 24, 2021	Few-Shot LearningPrompt Engineering	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