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 601650 of 2964 papers

TitleStatusHype
FAPIS: A Few-shot Anchor-free Part-based Instance SegmenterCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
Detecting Hate Speech with GPT-3Code1
Hypernetwork approach to Bayesian MAMLCode1
HyperShot: Few-Shot Learning by Kernel HyperNetworksCode1
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot LearningCode1
Feature Generation for Long-tail ClassificationCode1
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the CentroidCode1
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot LearningCode1
IM-IAD: Industrial Image Anomaly Detection Benchmark in ManufacturingCode1
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph PretrainingCode1
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary DataCode1
Exploring Efficient Few-shot Adaptation for Vision TransformersCode1
Cross-domain Few-shot Learning with Task-specific AdaptersCode1
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
In-Context Learning for Few-Shot Dialogue State TrackingCode1
Inductive Relation Prediction by BERTCode1
Information Maximization for Few-Shot LearningCode1
Instruction Tuning for Few-Shot Aspect-Based Sentiment AnalysisCode1
Integrative Few-Shot Learning for Classification and SegmentationCode1
Interventional Few-Shot LearningCode1
Intriguing Properties of Vision TransformersCode1
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsCode1
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot ClassificationCode1
CoNeRF: Controllable Neural Radiance FieldsCode1
kNN-NER: Named Entity Recognition with Nearest Neighbor SearchCode1
Label Semantics for Few Shot Named Entity RecognitionCode1
Explanation-Guided Training for Cross-Domain Few-Shot ClassificationCode1
Language Quantized AutoEncoders: Towards Unsupervised Text-Image AlignmentCode1
Laplacian Regularized Few-Shot LearningCode1
Data Distributional Properties Drive Emergent In-Context Learning in TransformersCode1
Extending Context Window of Large Language Models via Semantic CompressionCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and DatasetsCode1
Evaluating Weakly Supervised Object Localization Methods RightCode1
EventCLIP: Adapting CLIP for Event-based Object RecognitionCode1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter OptimizationCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Learning Multi-modal Representations by Watching Hundreds of Surgical Video LecturesCode1
Learning to Affiliate: Mutual Centralized Learning for Few-shot ClassificationCode1
Learning to Compare: Relation Network for Few-Shot LearningCode1
Enhancing Few-shot Image Classification with Cosine TransformerCode1
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language InferenceCode1
BankNote-Net: Open dataset for assistive universal currency recognitionCode1
Concept Learners for Few-Shot LearningCode1
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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