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 25012550 of 2964 papers

TitleStatusHype
Brain-inspired global-local learning incorporated with neuromorphic computing0
Interpretable Time-series Classification on Few-shot SamplesCode1
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot0
Few-Shot Pill Recognition0
METAL: Minimum Effort Temporal Activity Localization in Untrimmed Videos0
Adaptive Subspaces for Few-Shot LearningCode1
Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition0
Attentive Weights Generation for Few Shot Learning via Information MaximizationCode1
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation0
Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data0
Towards Context-Agnostic Learning Using Synthetic DataCode0
High-order structure preserving graph neural network for few-shot learningCode0
Language Models are Few-Shot LearnersCode3
Boosting Few-Shot Learning With Adaptive Margin Loss0
Looking back to lower-level information in few-shot learning0
SSM-Net for Plants Disease Identification in Low Data RegimeCode0
Pay Attention to What You Read: Non-recurrent Handwritten Text-Line Recognition0
Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors0
One of these (Few) Things is Not Like the Others0
Cross-Domain Few-Shot Learning with Meta Fine-Tuning0
Prototypical Q Networks for Automatic Conversational Diagnosis and Few-Shot New Disease Adaption0
Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning0
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification0
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational RepresentationsCode1
Boosting on the shoulders of giants in quantum device calibrationCode1
Dynamic Memory Induction Networks for Few-Shot Text Classification0
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing0
AdaDurIAN: Few-shot Adaptation for Neural Text-to-Speech with DurIAN0
SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine TeachingCode1
Supervision and Source Domain Impact on Representation Learning: A Histopathology Case StudyCode0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification0
Memory-Augmented Relation Network for Few-Shot Learning0
Harvesting and Refining Question-Answer Pairs for Unsupervised QACode1
DenoiSeg: Joint Denoising and SegmentationCode1
Gradual Relation Network: Decoding Intuitive Upper Extremity Movement Imaginations Based on Few-Shot EEG Learning0
Incremental Few-Shot Object Detection for Robotics0
Generalized Reinforcement Meta Learning for Few-Shot Optimization0
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification0
Towards Fast Adaptation of Neural Architectures with Meta LearningCode1
Few-Shot Learning for Opinion SummarizationCode1
Physarum Powered Differentiable Linear Programming Layers and ApplicationsCode1
Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review0
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense DisambiguationCode1
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection SegmentationCode1
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data0
Supervised Domain Adaptation: A Graph Embedding Perspective and a Rectified Experimental ProtocolCode1
Learning to Classify Intents and Slot Labels Given a Handful of Examples0
Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification0
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition0
A Comprehensive Overview and Survey of Recent Advances in Meta-Learning0
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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