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 10511100 of 2964 papers

TitleStatusHype
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning0
Bridging Modalities: Enhancing Cross-Modality Hate Speech Detection with Few-Shot In-Context Learning0
From Dataset to Real-world: General 3D Object Detection via Generalized Cross-domain Few-shot Learning0
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing0
Brain-inspired global-local learning incorporated with neuromorphic computing0
Early-Stopping for Meta-Learning: Estimating Generalization from the Activation Dynamics0
Dynamic Memory Induction Networks for Few-Shot Text Classification0
Dynamic Input Structure and Network Assembly for Few-Shot Learning0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset0
Frequency Guidance Matters in Few-Shot Learning0
Dynamic Few-Shot Learning for Knowledge Graph Question Answering0
Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications0
Language Models are Few-shot Learners for Prognostic Prediction0
FrLove : Could a Frenchman rapidly identify Lovecraft?0
From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models0
From User Preferences to Optimization Constraints Using Large Language Models0
Fully Fine-tuned CLIP Models are Efficient Few-Shot Learners0
Generating Synthetic Datasets for Few-shot Prompt Tuning0
HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes0
Dynamic Conditional Networks for Few-Shot Learning0
Boosting Transductive Few-Shot Fine-Tuning With Margin-Based Uncertainty Weighting and Probability Regularization0
Advances in MetaDL: AAAI 2021 challenge and workshop0
Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning0
Dual-channel Prototype Network for few-shot Classification of Pathological Images0
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning0
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning0
Boosting Supervision with Self-Supervision for Few-shot Learning0
DrugLLM: Open Large Language Model for Few-shot Molecule Generation0
Dropping Networks for Transfer Learning0
An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis0
Boosting Meta-Training with Base Class Information for Few-Shot Learning0
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation0
Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning0
Probing Few-Shot Generalization with Attributes0
Do Prompt-Based Models Really Understand the Meaning of Their Prompts?0
Advance Fake Video Detection via Vision Transformers0
Across-Game Engagement Modelling via Few-Shot Learning0
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection0
Domain-invariant Prototypes for Semantic Segmentation0
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning0
Flexible and Scalable Deep Dendritic Spiking Neural Networks with Multiple Nonlinear Branching0
FoMo: A Foundation Model for Mobile Traffic Forecasting with Diffusion Model0
Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction0
Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification0
Domain Agnostic Few-shot Learning for Speaker Verification0
Domain Agnostic Few-Shot Learning For Document Intelligence0
A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning0
Boosting Few-Shot Learning With Adaptive Margin Loss0
Domain Adaptation for Learning Generator from Paired Few-Shot Data0
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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