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 1251–1275 of 2964 papers

Title	Date	Tasks	Status	Hype
ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition	Jan 24, 2023	Domain AdaptationFew-Shot Learning	—Unverified	0
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
AI of Brain and Cognitive Sciences: From the Perspective of First Principles	Jan 20, 2023	Few-Shot Learningimage-classification	—Unverified	0
Exploiting Style Transfer-based Task Augmentation for Cross-Domain Few-Shot Learning	Jan 19, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
Concept Discovery for Fast Adapatation	Jan 19, 2023	Few-Shot LearningMeta-Learning	—Unverified	0
Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models	Jan 16, 2023	Audio ClassificationFew-Shot Learning	CodeCode Available	2
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
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning	Jan 11, 2023	class-incremental learningClass Incremental Learning	—Unverified	0
Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings	Jan 11, 2023	Few-Shot LearningStance Detection	—Unverified	0
Exploring Efficient Few-shot Adaptation for Vision Transformers	Jan 6, 2023	Few-Shot Learning	CodeCode Available	1
L-HYDRA: Multi-Head Physics-Informed Neural Networks	Jan 5, 2023	Few-Shot LearningMeta-Learning	CodeCode Available	0
High-level semantic feature matters few-shot unsupervised domain adaptation	Jan 5, 2023	Domain AdaptationFew-Shot Learning	—Unverified	0
Task Weighting in Meta-learning with Trajectory Optimisation	Jan 4, 2023	Few-Shot LearningMeta-Learning	—Unverified	0
A Theory of Human-Like Few-Shot Learning	Jan 3, 2023	Common Sense ReasoningFew-Shot Learning	—Unverified	0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge	Jan 3, 2023	Few-Shot LearningGraph Representation Learning	—Unverified	0
P3DC-Shot: Prior-Driven Discrete Data Calibration for Nearest-Neighbor Few-Shot Classification	Jan 2, 2023	ClassificationFew-Shot Learning	CodeCode Available	0
Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification	Jan 1, 2023	Few-Shot Image ClassificationFew-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
Task-aware Adaptive Learning for Cross-domain Few-shot Learning	Jan 1, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified	0
Frequency Guidance Matters in Few-Shot Learning	Jan 1, 2023	Few-Shot LearningMetric Learning	—Unverified	0
Few-shot Continual Infomax Learning	Jan 1, 2023	Continual LearningFew-Shot Learning	—Unverified	0
Revisiting Prototypical Network for Cross Domain Few-Shot Learning	Jan 1, 2023	Cross-Domain Few-Shotcross-domain few-shot learning	CodeCode Available	1
Bi-Level Meta-Learning for Few-Shot Domain Generalization	Jan 1, 2023	Domain GeneralizationFew-Shot Learning	—Unverified	0

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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