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 1526–1550 of 2964 papers

Title	Date	Tasks	Status	Hype
Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification	Jun 30, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
Domain Agnostic Few-shot Learning for Speaker Verification	Jun 28, 2022	Domain GeneralizationFew-Shot Learning	—Unverified	0
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding	Jun 27, 2022	Few-Shot Learningtext-to-speech	—Unverified	0
Understanding Benign Overfitting in Gradient-Based Meta Learning	Jun 27, 2022	Few-Shot LearningLearning Theory	—Unverified	0
Prompting Decision Transformer for Few-Shot Policy Generalization	Jun 27, 2022	Few-Shot LearningInductive Bias	—Unverified	0
Few-Shot Stance Detection via Target-Aware Prompt Distillation	Jun 27, 2022	Few-Shot LearningFew-Shot Stance Detection	—Unverified	0
Task-Adaptive Few-shot Node Classification	Jun 23, 2022	ClassificationFew-Shot Learning	CodeCode Available	1
Single Morphing Attack Detection using Siamese Network and Few-shot Learning	Jun 22, 2022	Face Morphing Attack DetectionFace Verification	—Unverified	0
Low Resource Pipeline for Spoken Language Understanding via Weak Supervision	Jun 21, 2022	Emotion ClassificationFew-Shot Learning	—Unverified	0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning	Jun 21, 2022	Contrastive LearningDomain Adaptation	CodeCode Available	0
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification	Jun 20, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
C^-algebra Net: A New Approach Generalizing Neural Network Parameters to C^-algebra	Jun 20, 2022	Density EstimationFew-Shot Learning	—Unverified	0
Model-Agnostic Few-Shot Open-Set Recognition	Jun 18, 2022	Few-Shot Learningmodel	CodeCode Available	1
EEML: Ensemble Embedded Meta-learning	Jun 18, 2022	Few-Shot LearningMeta-Learning	—Unverified	0
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning	Jun 17, 2022	Few-Shot LearningOffline RL	CodeCode Available	2
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification	Jun 17, 2022	Federated LearningFew-Shot Learning	CodeCode Available	1
Lifelong Wandering: A realistic few-shot online continual learning setting	Jun 16, 2022	Continual LearningFew-Shot Learning	—Unverified	0
Channel Importance Matters in Few-Shot Image Classification	Jun 16, 2022	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification	Jun 15, 2022	Few-Shot Learningimage-classification	—Unverified	0
Rethinking Generalization in Few-Shot Classification	Jun 15, 2022	ClassificationFew-Shot Image Classification	CodeCode Available	1
NatGen: Generative pre-training by "Naturalizing" source code	Jun 15, 2022	Code GenerationCode Translation	CodeCode Available	1
Language Models are General-Purpose Interfaces	Jun 13, 2022	Causal Language ModelingFew-Shot Learning	—Unverified	0
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams	Jun 11, 2022	BIG-bench Machine LearningFew-Shot Learning	—Unverified	0
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators	Jun 10, 2022	Few-Shot LearningQuantization	—Unverified	0
Neural Prompt Search	Jun 9, 2022	Few-Shot LearningImage Classification	CodeCode Available	2

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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