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 26512700 of 2964 papers

TitleStatusHype
Prototype Rectification for Few-Shot Learning0
Generalized Adaptation for Few-Shot Learning0
Facial Landmark Correlation Analysis0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning0
Meta Adaptation using Importance Weighted Demonstrations0
Differentiable Meta-learning Model for Few-shot Semantic Segmentation0
A Conceptual Framework for Lifelong Learning0
Knowledge Graph Transfer Network for Few-Shot RecognitionCode0
Learning to Control Latent Representations for Few-Shot Learning of Named Entities0
Program synthesis performance constrained by non-linear spatial relations in Synthetic Visual Reasoning TestCode0
Defensive Few-shot LearningCode0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot LearningCode1
Learning from the Past: Continual Meta-Learning via Bayesian Graph Modeling0
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive BaselinesCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Penalty Method for Inversion-Free Deep Bilevel OptimizationCode1
On-Device Machine Learning: An Algorithms and Learning Theory Perspective0
Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Events0
Multimodal Model-Agnostic Meta-Learning via Task-Aware ModulationCode1
A Neural Topic-Attention Model for Medical Term Abbreviation DisambiguationCode0
AMP0: Species-Specific Prediction of Anti-microbial Peptides using Zero and Few Shot Learning0
Shoestring: Graph-Based Semi-Supervised Learning with Severely Limited Labeled DataCode0
Neural Similarity LearningCode0
Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event DetectionCode0
Texture Bias Of CNNs Limits Few-Shot Classification Performance0
Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network0
Artistic Glyph Image Synthesis via One-Stage Few-Shot LearningCode1
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor0
Bayesian Meta-Learning for the Few-Shot Setting via Deep KernelsCode1
Semi Few-Shot Attribute Translation0
When Does Self-supervision Improve Few-shot Learning?Code0
Graph Few-shot Learning via Knowledge TransferCode0
Meta-Transfer Learning through Hard TasksCode1
Compositional Generalization for Primitive SubstitutionsCode0
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning0
Few-Shot Abstract Visual Reasoning With Spectral Features0
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
Collect and Select: Semantic Alignment Metric Learning for Few-Shot LearningCode0
Meta-Learning to Detect Rare Objects0
Variational Few-Shot Learning0
SILCO: Show a Few Images, Localize the Common Object0
Graph convolutional networks for learning with few clean and many noisy labelsCode0
Bad Form: Comparing Context-Based and Form-Based Few-Shot Learning in Distributional Semantic Models0
Revisiting Fine-tuning for Few-shot Learning0
Meta-learning algorithms for Few-Shot Computer VisionCode0
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning0
BEAN: Interpretable Representation Learning with Biologically-Enhanced Artificial Neuronal Assembly Regularization0
RLBench: The Robot Learning Benchmark & Learning EnvironmentCode0
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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