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 151200 of 2964 papers

TitleStatusHype
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
Expanding Event Modality Applications through a Robust CLIP-Based EncoderCode1
Few-shot Adaptation Works with UnpredicTable DataCode1
Calibrate Before Use: Improving Few-Shot Performance of Language ModelsCode1
Dual Adaptive Representation Alignment for Cross-domain Few-shot LearningCode1
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled DataCode1
Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical ImagingCode1
Domain-Adaptive Few-Shot LearningCode1
DPGN: Distribution Propagation Graph Network for Few-shot LearningCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
Disentangled Feature Representation for Few-shot Image ClassificationCode1
Disentangle and Remerge: Interventional Knowledge Distillation for Few-Shot Object Detection from A Conditional Causal PerspectiveCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Can Explanations Be Useful for Calibrating Black Box Models?Code1
Boosting Few-Shot Classification with View-Learnable Contrastive LearningCode1
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot LearningCode1
BOIL: Towards Representation Change for Few-shot LearningCode1
EASE: Unsupervised Discriminant Subspace Learning for Transductive Few-Shot LearningCode1
BioBERT: a pre-trained biomedical language representation model for biomedical text miningCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot LearningCode1
Binocular Mutual Learning for Improving Few-shot ClassificationCode1
DiffCLIP: Few-shot Language-driven Multimodal ClassifierCode1
AAPL: Adding Attributes to Prompt Learning for Vision-Language ModelsCode1
Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real WorldCode1
Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground AlignmentCode1
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query ShiftCode1
Boosting on the shoulders of giants in quantum device calibrationCode1
Borrowing Knowledge From Pre-trained Language Model: A New Data-efficient Visual Learning ParadigmCode1
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual NoiseCode1
Bias-Eliminated Semantic Refinement for Any-Shot LearningCode1
Bridging Molecular Graphs and Large Language ModelsCode1
BSNet: Bi-Similarity Network for Few-shot Fine-grained Image ClassificationCode1
Domain Adaptive Few-Shot Open-Set LearningCode1
An Embarrassingly Simple Approach to Semi-Supervised Few-Shot LearningCode1
CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural RenderingCode1
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language ModelsCode1
Diagnosing Infeasible Optimization Problems Using Large Language ModelsCode1
A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human LevelCode1
Bi-directional Feature Reconstruction Network for Fine-Grained Few-Shot Image ClassificationCode1
Overcoming challenges in leveraging GANs for few-shot data augmentationCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
A Broader Study of Cross-Domain Few-Shot LearningCode1
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERTCode1
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot LearningCode1
Bitwidth-Adaptive Quantization-Aware Neural Network Training: A Meta-Learning ApproachCode1
Discrete and Soft Prompting for Multilingual ModelsCode1
Chameleon: A MatMul-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential DataCode1
A Modern Self-Referential Weight Matrix That Learns to Modify ItselfCode1
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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