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 2101–2125 of 2964 papers

Title	Date	Tasks	Status
Prototypical Verbalizer for Prompt-based Few-shot Tuning	Nov 16, 2021	Contrastive LearningEntity Typing	—Unverified
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection	Nov 16, 2021	ArticlesFew-Shot Learning	—Unverified
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High German	Nov 16, 2021	Few-Shot LearningWord Embeddings	—Unverified
NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction	Nov 16, 2021	Few-Shot LearningFew-shot NER	—Unverified
Towards Unified Prompt Tuning for Few-shot Learning	Nov 16, 2021	Few-Shot LearningLanguage Modeling	—Unverified
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness	Nov 16, 2021	Few-Shot LearningFew-shot NER	—Unverified
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis	Nov 16, 2021	Few-Shot LearningMeta-Learning	—Unverified
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning	Nov 16, 2021	Few-Shot LearningMeta-Learning	—Unverified
Can Explanations Be Useful for Calibrating Black Box Models?	Nov 16, 2021	Extractive Question-AnsweringFew-Shot Learning	—Unverified
Learning to Learn Recognising Biomedical Entities from Multiple Domains with Task Hardness	Nov 16, 2021	Domain AdaptationFew-Shot Learning	—Unverified
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models	Nov 16, 2021	Few-Shot Learning	—Unverified
Inverse is Better! Fast and Accurate Prompt for Slot Tagging	Nov 16, 2021	Few-Shot LearningPrediction	—Unverified
A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification	Nov 16, 2021	Few-Shot LearningFew-Shot Text Classification	—Unverified
Making Small Language Models Better Few-Shot Learners	Nov 16, 2021	Few-Shot LearningKnowledge Distillation	—Unverified
On the Multilingual Capabilities of Very Large-Scale English Language Models	Nov 16, 2021	Extractive Question-AnsweringFew-Shot Learning	—Unverified
Prompt-Guided Few-Shot Event Detection	Nov 16, 2021	Event DetectionEvent Extraction	—Unverified
Empirical Evaluation of Topic Zero- and Few-Shot Learning for Stance Dissonance Detection	Nov 16, 2021	Few-Shot LearningStance Detection	—Unverified
Bi-Matching Mechanism to Combat the Long Tail of Word Sense Disambiguation	Nov 16, 2021	Few-Shot LearningWord Sense Disambiguation	CodeCode Available
Cross-domain Named Entity Recognition via Graph Matching	Nov 16, 2021	Cross-Domain Named Entity RecognitionFew-Shot Learning	—Unverified
Few-Shot Knowledge Graph Completion with Data Fusion and Augmentation	Nov 16, 2021	Few-Shot LearningKnowledge Graph Completion	—Unverified
CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings	Nov 15, 2021	Continual LearningFew-Shot Learning	CodeCode Available
Scaling ASR Improves Zero and Few Shot Learning	Nov 10, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy	Nov 8, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Crowdsourcing with Meta-Workers: A New Way to Save the Budget	Nov 7, 2021	Few-Shot Learningimage-classification	—Unverified
Diverse Distributions of Self-Supervised Tasks for Meta-Learning in NLP	Nov 2, 2021	DiversityFew-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