SOTAVerified

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

Showing 151200 of 2964 papers

TitleStatusHype
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
Few-shot Adaptation Works with UnpredicTable DataCode1
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual NoiseCode1
DiffCLIP: Few-shot Language-driven Multimodal ClassifierCode1
Discrete and Soft Prompting for Multilingual ModelsCode1
DETReg: Unsupervised Pretraining with Region Priors for Object DetectionCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Diagnosing Infeasible Optimization Problems Using Large Language ModelsCode1
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language InferenceCode1
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and SynthesisCode1
Deeply Coupled Cross-Modal Prompt LearningCode1
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
Data Distributional Properties Drive Emergent In-Context Learning in TransformersCode1
A Rationale-Centric Framework for Human-in-the-loop Machine LearningCode1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
DenoiSeg: Joint Denoising and SegmentationCode1
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot LearningCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Covariate Distribution Aware Meta-learningCode1
Cross-Domain Few-Shot Learning by Representation FusionCode1
Cross-Modal Augmentation for Few-Shot Multimodal Fake News DetectionCode1
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese NetworkCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
A Prompt Learning Framework for Source Code SummarizationCode1
Convolutional Bypasses Are Better Vision Transformer AdaptersCode1
DC-SAM: In-Context Segment Anything in Images and Videos via Dual ConsistencyCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Deep Metric Learning for Open World Semantic SegmentationCode1
Defining Benchmarks for Continual Few-Shot LearningCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy DataCode1
Detecting Hate Speech with GPT-3Code1
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot LearningCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
AskIt: Unified Programming Interface for Programming with Large Language ModelsCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
Domain-Adaptive Few-Shot LearningCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERTCode1
An Explanation of In-context Learning as Implicit Bayesian InferenceCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91Unverified
2gpt-4-0125-previewAccuracy52.49Unverified
3gpt-3.5-turboAccuracy41.48Unverified
4gpt-3.5-turboAccuracy37.06Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63Unverified
6yikuan8/Clinical-LongformerAccuracy25.55Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06Unverified
8yikuan8/Clinical-LongformerAccuracy25.04Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27Unverified
2SaSPA + CAL4-shot Accuracy48.3Unverified
3Real-Guidance + CAL4-shot Accuracy41.5Unverified
4CAL4-shot Accuracy40.9Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2Unverified
2CALHarmonic mean35.2Unverified
3Variational Prompt TuningHarmonic mean34.69Unverified
4Real-Guidance + CALHarmonic mean34.5Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7Unverified
2TIM-GDAccuracy87.4Unverified
3UNEM-GaussianAccuracy66.4Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75Unverified
2HCTransformers5 way 1~2 shot74.74Unverified
3HyperShotAccuracy53.18Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7Unverified
2Real-Guidance + CAL4-shot Accuracy44.3Unverified
3CAL4-shot Accuracy42.2Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74Unverified
2DPGNAcc67.6Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79Unverified