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

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Showing 1601–1650 of 2964 papers

TitleStatusHype
Few-shot Learning by Exploiting Visual Concepts within CNNs—0
Few-shot Learning by Focusing on Differences—0
Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries—0
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine—0
Few-Shot Learning for Biometric Verification—0
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection—0
Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks—0
Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings—0
Few-shot Learning for CT Scan based COVID-19 Diagnosis—0
Few-shot Learning for Domain-specific Fine-grained Image Classification—0
Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring—0
Few Shot Learning for Information Verification—0
Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework—0
Few-shot learning for medical text: A systematic review—0
Few-shot Learning for Multi-label Intent Detection—0
Few-Shot Learning for Road Object Detection—0
Few-shot Learning for Slot Tagging with Attentive Relational Network—0
Few-shot Learning for Spatial Regression—0
Few-shot Learning for Sumerian Named Entity Recognition—0
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors—0
Few-shot Learning for Time-series Forecasting—0
Few-shot Learning for Topic Modeling—0
Few-shot Learning for Unsupervised Feature Selection—0
When Few-Shot Learning Meets Video Object Detection—0
Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI—0
Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation—0
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification—0
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Few-Shot Learning of Force-Based Motions From Demonstration Through Pre-training of Haptic Representation—0
Few-shot learning of neural networks from scratch by pseudo example optimization—0
Few-shot learning of new sound classes for target sound extraction—0
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation—0
Few-Shot Learning of Visual Compositional Concepts through Probabilistic Schema Induction—0
Few-Shot Learning on Graphs—0
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects—0
Few-Shot Learning Through an Information Retrieval Lens—0
Few-shot Learning using Data Augmentation and Time-Frequency Transformation for Time Series Classification—0
Few-shot learning using pre-training and shots, enriched by pre-trained samples—0
Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis—0
Few-shot Learning via Dirichlet Tessellation Ensemble—0
Few-Shot Learning via Learning the Representation, Provably—0
Few-Shot Learning via Saliency-guided Hallucination of Samples—0
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs—0
Few-shot learning with attention-based sequence-to-sequence models—0
Few-shot Learning with Big Prototypes—0
Towards Contextual Learning in Few-shot Object Classification—0
Few-shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition—0
Few-Shot Learning with Embedded Class Models and Shot-Free Meta Training—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1gpt-4-0125-previewAccuracy61.91—Unverified
2gpt-4-0125-previewAccuracy52.49—Unverified
3gpt-3.5-turboAccuracy41.48—Unverified
4gpt-3.5-turboAccuracy37.06—Unverified
5johnsnowlabs/JSL-MedMNX-7BAccuracy25.63—Unverified
6yikuan8/Clinical-LongformerAccuracy25.55—Unverified
7BioMistral/BioMistral-7B-DAREAccuracy25.06—Unverified
8yikuan8/Clinical-LongformerAccuracy25.04—Unverified
9PharMolix/BioMedGPT-LM-7BAccuracy24.92—Unverified
10PharMolix/BioMedGPT-LM-7BAccuracy24.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean67.27—Unverified
2SaSPA + CAL4-shot Accuracy48.3—Unverified
3Real-Guidance + CAL4-shot Accuracy41.5—Unverified
4CAL4-shot Accuracy40.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CALHarmonic mean52.2—Unverified
2CALHarmonic mean35.2—Unverified
3Variational Prompt TuningHarmonic mean34.69—Unverified
4Real-Guidance + CALHarmonic mean34.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BGNNAccuracy92.7—Unverified
2TIM-GDAccuracy87.4—Unverified
3UNEM-GaussianAccuracy66.4—Unverified
#ModelMetricClaimedVerifiedStatus
1EASY (transductive)Accuracy82.75—Unverified
2HCTransformers5 way 1~2 shot74.74—Unverified
3HyperShotAccuracy53.18—Unverified
#ModelMetricClaimedVerifiedStatus
1SaSPA + CAL4-shot Accuracy66.7—Unverified
2Real-Guidance + CAL4-shot Accuracy44.3—Unverified
3CAL4-shot Accuracy42.2—Unverified
#ModelMetricClaimedVerifiedStatus
1HCTransformersAcc74.74—Unverified
2DPGNAcc67.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MetaGen Blended RAG (zero-shot)Accuracy77.9—Unverified
2CoT-T5-11B (1024 Shot)Accuracy73.42—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.44—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy68.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean81.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean91.57—Unverified
#ModelMetricClaimedVerifiedStatus
1CovidExpertAUC-ROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1CoT-T5-11B (1024 Shot)Accuracy78.02—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy65.7—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy73.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean96.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean73.07—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean78.51—Unverified
#ModelMetricClaimedVerifiedStatus
1UNEM-GaussianAccuracy52.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Variational Prompt TuningHarmonic mean79—Unverified