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 276–300 of 2964 papers

Title	Date	Tasks	Status	Hype
STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables	Mar 2, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot Learning	Feb 28, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations	Feb 27, 2023	Contrastive LearningFew-Shot Learning	CodeCode Available	1
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning	Feb 18, 2023	Adversarial AttackCross-Domain Few-Shot	CodeCode Available	1
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification	Feb 16, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Meta-Learning Siamese Network for Few-Shot Text Classification	Feb 5, 2023	ClassificationDescriptive	CodeCode Available	1
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications	Feb 5, 2023	Few-Shot LearningImage Classification	CodeCode Available	1
Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment	Feb 2, 2023	AttributeFew-Shot Image Classification	CodeCode Available	1
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data	Feb 1, 2023	Few-Shot Learning	CodeCode Available	1
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing	Jan 31, 2023	Anomaly DetectionContinual Learning	CodeCode Available	1
Contrastive Meta-Learning for Partially Observable Few-Shot Learning	Jan 30, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	1
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning	Jan 27, 2023	Few-Shot LearningGSM8K	CodeCode Available	1
Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa	Jan 24, 2023	Few-Shot LearningPlant Phenotyping	CodeCode Available	1
Open-Set Likelihood Maximization for Few-Shot Learning	Jan 20, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP	Jan 12, 2023	3D Semantic SegmentationContrastive Learning	CodeCode Available	1
See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning	Jan 12, 2023	Few-Shot LearningImage Captioning	CodeCode Available	1
Exploring Efficient Few-shot Adaptation for Vision Transformers	Jan 6, 2023	Few-Shot Learning	CodeCode Available	1
Revisiting Prototypical Network for Cross Domain Few-Shot Learning	Jan 1, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud Classification	Jan 1, 2023	DescriptiveFew-Shot Learning	CodeCode Available	1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning Paradigm	Jan 1, 2023	Domain GeneralizationFew-Shot Learning	CodeCode Available	1
Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification	Jan 1, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Large Language Models are Better Reasoners with Self-Verification	Dec 19, 2022	Arithmetic ReasoningCommon Sense Reasoning	CodeCode Available	1
Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrations	Dec 19, 2022	Few-Shot LearningIn-Context Learning	CodeCode Available	1
Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models	Dec 16, 2022	Few-Shot LearningGesture Recognition	CodeCode Available	1
Federated Few-Shot Learning for Mobile NLP	Dec 12, 2022	Few-Shot LearningPrivacy Preserving	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