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 1651–1700 of 2964 papers

TitleStatusHype
Few-Shot Learning with Geometric Constraints—0
Few-shot Learning with Global Relatedness Decoupled-Distillation—0
Few-shot learning with improved local representations via bias rectify module—0
Few-Shot Learning with Intra-Class Knowledge Transfer—0
Few-shot Learning with Meta Metric Learners—0
Few-Shot Learning with Metric-Agnostic Conditional Embeddings—0
Improving Few-shot Learning by Spatially-aware Matching and CrossTransformer—0
Few Shot Learning With No Labels—0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images—0
Few-Shot Learning with Per-Sample Rich Supervision—0
Few-Shot Learning with Siamese Networks and Label Tuning—0
Few Shot Learning with Simplex—0
Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data—0
Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
Few-shot Medical Image Segmentation with Cycle-resemblance Attention—0
Few-Shot Meta-Denoising—0
Few-shot Multi-hop Question Answering over Knowledge Base—0
Few-shot Multimodal Multitask Multilingual Learning—0
Few-Shot Multi-task Learning via Implicit regularization—0
Few-Shot Named Entity Recognition: An Empirical Baseline Study—0
Few-shot Named Entity Recognition with Cloze Questions—0
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness—0
Few-Shot Nested Named Entity Recognition—0
Few-shot Object Detection on Remote Sensing Images—0
Few-Shot Object Detection: Research Advances and Challenges—0
Few-Shot Object Detection with Foundation Models—0
Few-Shot Object Detection with Proposal Balance Refinement—0
Few-Shot Object Recognition from Machine-Labeled Web Images—0
Few-Shot Partial-Label Learning—0
Few-Shot Pill Recognition—0
Few-shot pixel-precise document layout segmentation via dynamic instance generation and local thresholding—0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention—0
Few-shot Query-Focused Summarization with Prefix-Merging—0
Few Shot Rationale Generation using Self-Training with Dual Teachers—0
Few-Shot Regression via Learned Basis Functions—0
Few-Shot Regression via Learning Sparsifying Basis Functions—0
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation—0
Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges—0
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding—0
Few-shot Sequence Learning with Transformers—0
Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive Network—0
Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention—0
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification—0
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning—0
Meta-DM: Applications of Diffusion Models on Few-Shot Learning—0
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation—0
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning—0
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning—0
Meta-free few-shot learning via representation learning with weight averaging—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