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 201–225 of 2964 papers

Title	Date	Tasks	Status	Hype	Score
Adversarial Feature Augmentation for Cross-domain Few-shot Classification	Aug 23, 2022	ClassificationCross-Domain Few-Shot	CodeCode Available	1	5
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot Learning	Apr 26, 2023	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
Adversarial Feature Hallucination Networks for Few-Shot Learning	Mar 30, 2020	Data AugmentationDiversity	CodeCode Available	1	5
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal Perspective	Aug 26, 2022	Few-Shot LearningFew-Shot Object Detection	CodeCode Available	1	5
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels	Mar 3, 2022	Anomaly DetectionCardiac Segmentation	CodeCode Available	1	5
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework	Aug 27, 2021	Anomaly DetectionDefect Detection	CodeCode Available	1	5
Domain-Adaptive Few-Shot Learning	Mar 19, 2020	Domain AdaptationFew-Shot Learning	CodeCode Available	1	5
Code Summarization Beyond Function Level	Feb 23, 2025	Code SummarizationFew-Shot Learning	CodeCode Available	1	5
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning	Mar 1, 2021	Few-Shot Image ClassificationFew-Shot Learning	CodeCode Available	1	5
Exploring Efficient Few-shot Adaptation for Vision Transformers	Jan 6, 2023	Few-Shot Learning	CodeCode Available	1	5
An Overview of Deep Learning Architectures in Few-Shot Learning Domain	Aug 12, 2020	Deep LearningFew-Shot Learning	CodeCode Available	1	5
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation	Jun 1, 2021	Few-Shot LearningFew-Shot Semantic Segmentation	CodeCode Available	1	5
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors	May 9, 2023	Code GenerationFew-Shot Learning	CodeCode Available	1	5
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs	May 5, 2022	ClassificationFew-Shot Learning	CodeCode Available	1	5
A Modern Self-Referential Weight Matrix That Learns to Modify Itself	Feb 11, 2022	Few-Shot Learning	CodeCode Available	1	5
Feature Generation for Long-tail Classification	Nov 10, 2021	ClassificationFew-Shot Learning	CodeCode Available	1	5
Federated Few-shot Learning	Jun 17, 2023	ArticlesFederated Learning	CodeCode Available	1	5
Federated Few-Shot Learning for Mobile NLP	Dec 12, 2022	Few-Shot LearningPrivacy Preserving	CodeCode Available	1	5
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions	May 27, 2022	BenchmarkingFew-Shot Image Classification	CodeCode Available	1	5
Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World	Oct 16, 2023	Few-Shot LearningForm	CodeCode Available	1	5
FewSAR: A Few-shot SAR Image Classification Benchmark	Jun 16, 2023	ClassificationFew-Shot Learning	CodeCode Available	1	5
Few-shot Adaptation Works with UnpredicTable Data	Aug 1, 2022	Domain AdaptationFew-Shot Learning	CodeCode Available	1	5
A Rationale-Centric Framework for Human-in-the-loop Machine Learning	Mar 24, 2022	BIG-bench Machine LearningFew-Shot Learning	CodeCode Available	1	5
A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition	Sep 26, 2020	Few-Shot LearningFew-Shot Object Detection	CodeCode Available	1	5
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning Approach	Jul 20, 2022	Few-Shot LearningMeta-Learning	CodeCode Available	1	5

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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