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 1601–1625 of 2964 papers

Title	Date	Tasks	Status
RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for Mario	May 16, 2023	Few-Shot LearningMeta-Learning	CodeCode Available
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification	May 15, 2023	AllFew-Shot Image Classification	—Unverified
Learning More Discriminative Local Descriptors for Few-shot Learning	May 15, 2023	Few-Shot Learningimage-classification	—Unverified
Meta-DM: Applications of Diffusion Models on Few-Shot Learning	May 14, 2023	Few-Shot LearningUnsupervised Few-Shot Image Classification	—Unverified
Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework	May 8, 2023	Few-Shot LearningMedical Image Analysis	—Unverified
The Role of Data Curation in Image Captioning	May 5, 2023	Few-Shot LearningImage Captioning	CodeCode Available
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models	May 4, 2023	Few-Shot LearningGraph Clustering	CodeCode Available
Plug-and-Play Multilingual Few-shot Spoken Words Recognition	May 3, 2023	Few-Shot LearningKeyword Spotting	—Unverified
Alleviating Exposure Bias via Multi-level Contrastive Learning and Deviation Simulation in Abstractive Summarization	May 3, 2023	Abstractive Text SummarizationContrastive Learning	CodeCode Available
Learning to Detect Novel and Fine-Grained Acoustic Sequences Using Pretrained Audio Representations	May 3, 2023	Event DetectionFew-Shot Learning	—Unverified
Causal Interventions-based Few-Shot Named Entity Recognition	May 3, 2023	Few-Shot Learningfew-shot-ner	—Unverified
DocLangID: Improving Few-Shot Training to Identify the Language of Historical Documents	May 3, 2023	Few-Shot LearningLanguage Identification	CodeCode Available
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking	May 3, 2023	Few-Shot LearningQuestion Answering	CodeCode Available
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment	May 2, 2023	Cross-Lingual TransferFew-Shot Learning	CodeCode Available
Accelerating Neural Self-Improvement via Bootstrapping	May 2, 2023	Few-Shot LearningMeta-Learning	—Unverified
HQP: A Human-Annotated Dataset for Detecting Online Propaganda	Apr 28, 2023	Few-Shot LearningPropaganda detection	CodeCode Available
Analogy-Forming Transformers for Few-Shot 3D Parsing	Apr 27, 2023	Few-Shot LearningMeta-Learning	—Unverified
Adaptive manifold for imbalanced transductive few-shot learning	Apr 27, 2023	Few-Shot Learning	—Unverified
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection	Apr 24, 2023	Data AugmentationFew-Shot Learning	—Unverified
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework	Apr 24, 2023	Few-Shot LearningMatrix Completion	—Unverified
Information Extraction from Documents: Question Answering vs Token Classification in real-world setups	Apr 21, 2023	ClassificationFew-Shot Learning	—Unverified
RPLKG: Robust Prompt Learning with Knowledge Graph	Apr 21, 2023	Domain GeneralizationFew-Shot Learning	—Unverified
Semantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition	Apr 21, 2023	Few-Shot LearningGraph Matching	CodeCode Available
Clustered-patch Element Connection for Few-shot Learning	Apr 20, 2023	Few-Shot LearningFew-Shot Semantic Segmentation	CodeCode Available
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning	Apr 19, 2023	Data AugmentationFew-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