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 12011250 of 2964 papers

TitleStatusHype
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion SegmentationCode0
CLR-GAM: Contrastive Point Cloud Learning with Guided Augmentation and Feature Mapping0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
Meta Learning to Bridge Vision and Language Models for Multimodal Few-Shot LearningCode1
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient RepresentationsCode1
LLaMA: Open and Efficient Foundation Language ModelsCode7
AugGPT: Leveraging ChatGPT for Text Data AugmentationCode0
Language Models are Few-shot Learners for Prognostic Prediction0
Pre-Finetuning for Few-Shot Emotional Speech RecognitionCode0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
One Fits All:Power General Time Series Analysis by Pretrained LMCode2
Sentence Simplification via Large Language ModelsCode0
KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting0
Mask-guided BERT for Few Shot Text Classification0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention0
Meta-World Conditional Neural Processes0
Few-shot Multimodal Multitask Multilingual Learning0
DGP-Net: Dense Graph Prototype Network for Few-Shot SAR Target Recognition0
Neural Attention Memory0
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot LearningCode1
An Adaptive Plug-and-Play Network for Few-Shot Learning0
Scalable Prompt Generation for Semi-supervised Learning with Language Models0
Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-190
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?0
Conversation Style Transfer using Few-Shot Learning0
Few-shot learning approaches for classifying low resource domain specific software requirements0
An Empirical Evaluation of Using Large Language Models for Automated Unit Test GenerationCode2
Distillation of encoder-decoder transformers for sequence labelling0
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning0
A Systematic Evaluation and Benchmark for Embedding-Aware Generative Models: Features, Models, and Any-shot Scenarios0
CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models0
Gestalt-Guided Image Understanding for Few-Shot LearningCode0
AutoWS: Automated Weak Supervision Framework for Text Classification0
Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNsCode0
Meta-Learning Siamese Network for Few-Shot Text ClassificationCode1
Revisiting Discriminative vs. Generative Classifiers: Theory and ImplicationsCode1
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property PredictionCode0
Language Quantized AutoEncoders: Towards Unsupervised Text-Image AlignmentCode1
The unreasonable effectiveness of few-shot learning for machine translation0
Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary DataCode1
Differentiable Entailment for Parameter Efficient Few Shot Learning0
IM-IAD: Industrial Image Anomaly Detection Benchmark in ManufacturingCode1
Contrastive Meta-Learning for Partially Observable Few-Shot LearningCode1
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context LearningCode1
Alignment with human representations supports robust few-shot learning0
Explore the Power of Dropout on Few-shot Learning0
FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs0
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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