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 751800 of 2964 papers

TitleStatusHype
Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot TechniquesCode0
ACE: Anatomically Consistent Embeddings in Composition and DecompositionCode0
CLOSURE: Assessing Systematic Generalization of CLEVR ModelsCode0
Make SVM great again with Siamese kernel for few-shot learningCode0
AffinityNet: semi-supervised few-shot learning for disease type predictionCode0
Closed-Form Feedback-Free Learning with Forward ProjectionCode0
CLoSE: Contrastive Learning of Subframe Embeddings for Political Bias Classification of News MediaCode0
Probing Predictions on OOD Images via Nearest CategoriesCode0
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer LearningCode0
Are LSTMs Good Few-Shot Learners?Code0
Low-Shot Learning for the Semantic Segmentation of Remote Sensing ImageryCode0
MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learningCode0
Are Large Language Models Robust Coreference Resolvers?Code0
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline TimepointCode0
MAD: Meta Adversarial Defense BenchmarkCode0
ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements?Code0
Logarithm-transform aided Gaussian Sampling for Few-Shot LearningCode0
LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine FeedbackCode0
A few-shot learning approach with domain adaptation for personalized real-life stress detection in close relationshipsCode0
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification TasksCode0
MATE: Plugging in Model Awareness to Task Embedding for Meta LearningCode0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningCode0
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable PromptingCode0
Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English LanguageCode0
APT: Architectural Planning and Text-to-Blueprint Construction Using Large Language Models for Open-World AgentsCode0
Limited Data Rolling Bearing Fault Diagnosis with Few-shot LearningCode0
Class-Agnostic CountingCode0
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and ClassificationCode0
A Few-Shot Attention Recurrent Residual U-Net for Crack SegmentationCode0
L-HYDRA: Multi-Head Physics-Informed Neural NetworksCode0
Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot LearningCode0
A Primal-Dual Subgradient Approachfor Fair Meta LearningCode0
Leveraging Bottom-Up and Top-Down Attention for Few-Shot Object DetectionCode0
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image UnderstandingCode0
Chatbots Are Not Reliable Text AnnotatorsCode0
AugGPT: Leveraging ChatGPT for Text Data AugmentationCode0
Two Sides of Meta-Learning Evaluation: In vs. Out of DistributionCode0
LGM-Net: Learning to Generate Matching Networks for Few-Shot LearningCode0
Learning to Propagate for Graph Meta-LearningCode0
Learning to Propagate Labels: Transductive Propagation Network for Few-shot LearningCode0
A Feature Generator for Few-Shot LearningCode0
CFReID: Continual Few-shot Person Re-IdentificationCode0
Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-TimeCode0
Certification of Speaker Recognition Models to Additive PerturbationsCode0
Learning to Learn Variational Semantic MemoryCode0
Accelerating Convergence in Bayesian Few-Shot ClassificationCode0
Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language UnderstandingCode0
Learning to Learn Kernels with Variational Random FeaturesCode0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
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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