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 2151–2175 of 2964 papers

Title	Date	Tasks	Status
Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning	Oct 13, 2021	Contrastive LearningFew-Shot Learning	—Unverified
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers	Oct 13, 2021	Few-Shot Learningimage-classification	—Unverified
Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI	Oct 12, 2021	Few-Shot LearningFew-Shot NLI	CodeCode Available
A Closer Look at Prototype Classifier for Few-shot Image Classification	Oct 11, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models	Oct 10, 2021	Few-Shot Learning	—Unverified
Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification	Oct 9, 2021	ClassificationFew-Shot Learning	CodeCode Available
One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction	Oct 6, 2021	Few-Shot LearningOne-Shot Learning	CodeCode Available
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint	Oct 6, 2021	Few-Shot LearningTrajectory Prediction	CodeCode Available
Multi-Objective Few-shot Learning for Fair Classification	Oct 5, 2021	AttributeClassification	—Unverified
Task Affinity with Maximum Bipartite Matching in Few-Shot Learning	Oct 5, 2021	Few-Shot Learning	CodeCode Available
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis	Oct 1, 2021	Few-Shot LearningSentiment Analysis	—Unverified
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing	Oct 1, 2021	Constituency ParsingFew-Shot Learning	—Unverified
Few-shot Learning with Big Prototypes	Sep 29, 2021	Few-Shot Learning	—Unverified
Loss meta-learning for forecasting	Sep 29, 2021	Few-Shot LearningMeta-Learning	—Unverified
Self-Supervised Prime-Dual Networks for Few-Shot Image Classification	Sep 29, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Few-shot Learning via Dirichlet Tessellation Ensemble	Sep 29, 2021	Few-Shot Learning	—Unverified
Refining Multimodal Representations using a modality-centric self-supervised module	Sep 29, 2021	Few-Shot Learning	—Unverified
Linear algebra with transformers	Sep 29, 2021	Automated Theorem ProvingFew-Shot Learning	—Unverified
Dataset Bias Prediction for Few-Shot Image Classification	Sep 29, 2021	ClassificationFew-Shot Image Classification	—Unverified
Assessing two novel distance-based loss functions for few-shot image classification	Sep 29, 2021	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Hierarchical Cross Contrastive Learning of Visual Representations	Sep 29, 2021	Contrastive LearningFew-Shot Learning	—Unverified
Few-shot graph link prediction with domain adaptation	Sep 29, 2021	Domain AdaptationFew-Shot Learning	—Unverified
Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis	Sep 29, 2021	Few-Shot LearningGeneralization Bounds	CodeCode Available
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics	Sep 29, 2021	Few-Shot LearningMeta-Learning	—Unverified
Robust Cross-Modal Semi-supervised Few Shot Learning	Sep 29, 2021	Few-Shot LearningGenerative Adversarial Network	—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