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 18511900 of 2964 papers

TitleStatusHype
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models0
OpineSum: Entailment-based self-training for abstractive opinion summarization0
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation0
Optimal Strategies to Perform Multilingual Analysis of Social Content for a Novel Dataset in the Tourism Domain0
Optimization of Image Embeddings for Few Shot Learning0
Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages0
Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning0
Out-of-distribution Few-shot Learning For Edge Devices without Model Fine-tuning0
Overcoming Data Scarcity in Scanning Tunnelling Microscopy Image Segmentation0
PAC-Bayes meta-learning with implicit task-specific posteriors0
PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven Perturbed Gradient Descent0
PALM: Few-Shot Prompt Learning for Audio Language Models0
Pareto Self-Supervised Training for Few-Shot Learning0
PARMESAN: Parameter-Free Memory Search and Transduction for Dense Prediction Tasks0
Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning0
Partitioning Image Representation in Contrastive Learning0
Partner-Assisted Learning for Few-Shot Image Classification0
PatchMix Augmentation to Identify Causal Features in Few-shot Learning0
PatchProto Networks for Few-shot Visual Anomaly Classification0
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition0
PeerArg: Argumentative Peer Review with LLMs0
PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC0
PEMNET: A Transfer Learning-based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems0
PennSyn2Real: Training Object Recognition Models without Human Labeling0
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction0
Personalizing Pre-trained Models0
PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning0
PEVA-Net: Prompt-Enhanced View Aggregation Network for Zero/Few-Shot Multi-View 3D Shape Recognition0
Physics-Driven Local-Whole Elastic Deformation Modeling for Point Cloud Representation Learning0
PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervision0
P4E: Few-Shot Event Detection as Prompt-Guided Identification and Localization0
PLOT-TAL -- Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization0
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification0
Plug-and-Play Multilingual Few-shot Spoken Words Recognition0
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification0
PNN: A Novel Progressive Neural Network for Fault Classification in Rotating Machinery under Small Dataset Constraint0
Point Cloud Understanding via Attention-Driven Contrastive Learning0
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection0
Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction0
Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling0
Power Normalizations in Fine-grained Image, Few-shot Image and Graph Classification0
Power Normalizing Second-order Similarity Network for Few-shot Learning0
PPT: Pre-trained Prompt Tuning for Few-shot Learning0
Predicting Electricity Consumption with Random Walks on Gaussian Processes0
Predictive Complexity Priors0
Predictive uncertainty estimation in deep learning for lung carcinoma classification in digital pathology under real dataset shifts0
Two-stage LLM Fine-tuning with Less Specialization and More Generalization0
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs0
Prior Knowledge for Few-shot Learning—Inductive Reasoning and Distribution Calibration0
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning0
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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