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 801–825 of 2964 papers

Title	Date	Tasks	Status	Hype
A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness	Dec 2, 2023	Few-Shot LearningIn-Context Learning	—Unverified	0
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language Alignment	Dec 1, 2023	Contrastive LearningFew-Shot Learning	CodeCode Available	3
Applying Large Language Models and Chain-of-Thought for Automatic Scoring	Nov 30, 2023	Few-Shot LearningPrompt Engineering	—Unverified	0
Simple Semantic-Aided Few-Shot Learning	Nov 30, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Clinical Risk Prediction Using Language Models: Benefits And Considerations	Nov 29, 2023	DiagnosticFew-Shot Learning	—Unverified	0
Explaining CLIP's performance disparities on data from blind/low vision users	Nov 29, 2023	Few-Shot Learningzero-shot-classification	—Unverified	0
A transductive few-shot learning approach for classification of digital histopathological slides from liver cancer	Nov 29, 2023	Few-Shot LearningTransductive Learning	—Unverified	0
ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?	Nov 28, 2023	Few-Shot LearningInformation Retrieval	CodeCode Available	0
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation	Nov 28, 2023	Few-Shot Learning	—Unverified	0
When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning	Nov 28, 2023	Few-Shot Learning	—Unverified	0
Target-Free Compound Activity Prediction via Few-Shot Learning	Nov 27, 2023	Activity PredictionDrug Discovery	—Unverified	0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations	Nov 27, 2023	DenoisingFew-Shot Learning	—Unverified	0
MEDITRON-70B: Scaling Medical Pretraining for Large Language Models	Nov 27, 2023	ArticlesConditional Text Generation	CodeCode Available	4
CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering	Nov 27, 2023	Few-Shot LearningNeRF	CodeCode Available	1
Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles	Nov 27, 2023	Few-Shot LearningZero-Shot Learning	—Unverified	0
Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination	Nov 27, 2023	Few-Shot LearningHallucination	—Unverified	0
Learning to Skip for Language Modeling	Nov 26, 2023	Few-Shot LearningLanguage Modeling	—Unverified	0
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection	Nov 26, 2023	Few-Shot LearningOut-of-Distribution Detection	CodeCode Available	1
Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View	Nov 24, 2023	Anomaly DetectionFew-Shot Learning	CodeCode Available	1
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning	Nov 24, 2023	Few-Shot Learning	CodeCode Available	0
A Multi-solution Study on GDPR AI-enabled Completeness Checking of DPAs	Nov 23, 2023	Few-Shot LearningLanguage Modeling	—Unverified	0
Language-guided Few-shot Semantic Segmentation	Nov 23, 2023	Few-Shot LearningFew-Shot Semantic Segmentation	—Unverified	0
Benchmarking Toxic Molecule Classification using Graph Neural Networks and Few Shot Learning	Nov 22, 2023	BenchmarkingDrug Discovery	—Unverified	0
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	0
NERIF: GPT-4V for Automatic Scoring of Drawn Models	Nov 21, 2023	Few-Shot Learning	—Unverified	0

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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