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 2751–2775 of 2964 papers

Title	Date	Tasks	Status
Boosting Few-Shot Visual Learning with Self-Supervision	Jun 12, 2019	Few-Shot LearningSelf-Supervised Learning	CodeCode Available
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction	Jun 12, 2019	Few-Shot LearningGraph Generation	—Unverified
Few-Shot Point Cloud Region Annotation with Human in the Loop	Jun 11, 2019	Few-Shot Learning	CodeCode Available
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition	Jun 11, 2019	Action RecognitionFew-Shot Image Classification	—Unverified
Few-Shot Learning with Per-Sample Rich Supervision	Jun 10, 2019	Few-Shot LearningGeneral Classification	—Unverified
Progressive Cluster Purification for Transductive Few-shot Learning	Jun 10, 2019	Few-Shot LearningGeneral Classification	—Unverified
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks	Jun 9, 2019	ColorizationFew-Shot Learning	CodeCode Available
Evolving Losses for Unlabeled Video Representation Learning	Jun 7, 2019	Few-Shot LearningMulti-Task Learning	—Unverified
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning	Jun 7, 2019	Few-Shot LearningGeneralized Few-Shot Learning	CodeCode Available
Adaptive Gradient-Based Meta-Learning Methods	Jun 6, 2019	Federated LearningFew-Shot Learning	CodeCode Available
Discriminative Few-Shot Learning Based on Directional Statistics	Jun 5, 2019	Few-Shot LearningGeneral Classification	—Unverified
Baby steps towards few-shot learning with multiple semantics	Jun 5, 2019	Few-Shot Image ClassificationFew-Shot Learning	—Unverified
Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study	Jun 4, 2019	Few-Shot LearningObject	—Unverified
Sequential Scenario-Specific Meta Learner for Online Recommendation	Jun 2, 2019	Few-Shot LearningMeta-Learning	CodeCode Available
Incremental Few-Shot Learning for Pedestrian Attribute Recognition	Jun 2, 2019	AttributeFew-Shot Learning	—Unverified
Second-order contexts from lexical substitutes for few-shot learning of word representations	Jun 1, 2019	Few-Shot LearningSentence	—Unverified
Task Agnostic Meta-Learning for Few-Shot Learning	Jun 1, 2019	Few-Shot LearningGeneral Classification	—Unverified
Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks	Jun 1, 2019	ColorizationFew-Shot Learning	CodeCode Available
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification	Jun 1, 2019	Data AugmentationFew-Shot Image Classification	—Unverified
Large-Scale Few-Shot Learning: Knowledge Transfer With Class Hierarchy	Jun 1, 2019	Few-Shot LearningTransfer Learning	CodeCode Available
Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders	Jun 1, 2019	Few-Shot LearningGeneralized Few-Shot Learning	CodeCode Available
Semantic Projection Network for Zero- and Few-Label Semantic Segmentation	Jun 1, 2019	Few-Shot LearningImage Segmentation	CodeCode Available
Regression Networks for Meta-Learning Few-Shot Classification	May 31, 2019	ClassificationFew-Shot Learning	CodeCode Available
Adaptive Deep Kernel Learning	May 28, 2019	BenchmarkingDrug Discovery	—Unverified
Dataset2Vec: Learning Dataset Meta-Features	May 27, 2019	Auxiliary LearningFew-Shot Learning	CodeCode Available

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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