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 27512800 of 2964 papers

TitleStatusHype
Limited Data Rolling Bearing Fault Diagnosis with Few-shot LearningCode0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Few-Shot Learning with Global Class RepresentationsCode0
Memory-Based Neighbourhood Embedding for Visual Recognition0
Meta Reasoning over Knowledge Graphs0
Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification0
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation0
Few-Shot Meta-Denoising0
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference0
Few-shot Learning for Domain-specific Fine-grained Image Classification0
Relational Generalized Few-Shot Learning0
Automatic detection of rare pathologies in fundus photographs using few-shot learning0
Unsupervised Task Design to Meta-Train Medical Image Classifiers0
Composing Neural Learning and Symbolic Reasoning with an Application to Visual DiscriminationCode0
Revisiting Metric Learning for Few-Shot Image Classification0
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation0
Few-Shot Video Classification via Temporal Alignment0
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding0
Boosting Supervision with Self-Supervision for Few-shot Learning0
Learning to Forget for Meta-LearningCode0
Interpretable Few-Shot Learning via Linear Distillation0
Learning Predicates as Functions to Enable Few-shot Scene Graph Prediction0
Boosting Few-Shot Visual Learning with Self-SupervisionCode0
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition0
Few-Shot Point Cloud Region Annotation with Human in the LoopCode0
Few-Shot Learning with Per-Sample Rich Supervision0
Progressive Cluster Purification for Transductive Few-shot Learning0
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented NetworksCode0
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
Evolving Losses for Unlabeled Video Representation Learning0
Adaptive Gradient-Based Meta-Learning MethodsCode0
Baby steps towards few-shot learning with multiple semantics0
Discriminative Few-Shot Learning Based on Directional Statistics0
Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study0
Sequential Scenario-Specific Meta Learner for Online RecommendationCode0
Incremental Few-Shot Learning for Pedestrian Attribute Recognition0
Coloring With Limited Data: Few-Shot Colorization via Memory Augmented NetworksCode0
Generalized Zero- and Few-Shot Learning via Aligned Variational AutoencodersCode0
Task Agnostic Meta-Learning for Few-Shot Learning0
Semantic Projection Network for Zero- and Few-Label Semantic SegmentationCode0
Large-Scale Few-Shot Learning: Knowledge Transfer With Class HierarchyCode0
Spot and Learn: A Maximum-Entropy Patch Sampler for Few-Shot Image Classification0
Second-order contexts from lexical substitutes for few-shot learning of word representations0
Regression Networks for Meta-Learning Few-Shot ClassificationCode0
Adaptive Deep Kernel Learning0
Dataset2Vec: Learning Dataset Meta-FeaturesCode0
A Plug-in Method for Representation Factorization in Connectionist ModelsCode0
Finding Task-Relevant Features for Few-Shot Learning by Category TraversalCode0
Constellation Loss: Improving the efficiency of deep metric learning loss functions for optimal embedding0
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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