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 101150 of 2964 papers

TitleStatusHype
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual NoiseCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Detecting Hate Speech with GPT-3Code1
Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy DataCode1
DETReg: Unsupervised Pretraining with Region Priors for Object DetectionCode1
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and SynthesisCode1
A Few-shot Learning Approach for Historical Ciphered Manuscript RecognitionCode1
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot LearningCode1
DeIL: Direct-and-Inverse CLIP for Open-World Few-Shot LearningCode1
Diagnosing Infeasible Optimization Problems Using Large Language ModelsCode1
BOIL: Towards Representation Change for Few-shot LearningCode1
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language ModelsCode1
Deep Metric Learning for Open World Semantic SegmentationCode1
Defining Benchmarks for Continual Few-Shot LearningCode1
DenoiSeg: Joint Denoising and SegmentationCode1
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
AdapterHub Playground: Simple and Flexible Few-Shot Learning with AdaptersCode1
DETA: Denoised Task Adaptation for Few-Shot LearningCode1
A Closer Look at Few-Shot 3D Point Cloud ClassificationCode1
DC-SAM: In-Context Segment Anything in Images and Videos via Dual ConsistencyCode1
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot LearningCode1
DiffCLIP: Few-shot Language-driven Multimodal ClassifierCode1
D^2ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action RecognitionCode1
Discrete and Soft Prompting for Multilingual ModelsCode1
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot LearningCode1
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal PerspectiveCode1
Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsCode1
Data Distributional Properties Drive Emergent In-Context Learning in TransformersCode1
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic SegmentationCode1
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPCode1
Cross-domain Few-shot Object Detection with Multi-modal Textual EnrichmentCode1
Cross-Domain Few-Shot Learning by Representation FusionCode1
CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese NetworkCode1
Cross-Modal Augmentation for Few-Shot Multimodal Fake News DetectionCode1
Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark StudyCode1
Cost-effective Instruction Learning for Pathology Vision and Language AnalysisCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identificationCode1
Adversarial Feature Augmentation for Cross-domain Few-shot ClassificationCode1
Contrastive Prototypical Network with Wasserstein Confidence PenaltyCode1
Convolutional Bypasses Are Better Vision Transformer AdaptersCode1
Covariate Distribution Aware Meta-learningCode1
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input RepresentationCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
GLAD: Content-aware Dynamic Graphs For Log Anomaly DetectionCode1
A Modern Self-Referential Weight Matrix That Learns to Modify ItselfCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Deeply Coupled Cross-Modal Prompt 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