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 19011950 of 2964 papers

TitleStatusHype
CS/NLP at SemEval-2022 Task 4: Effective Data Augmentation Methods for Patronizing Language Detection and Multi-label Classification with RoBERTa and GPT30
Few-shot Learning for Sumerian Named Entity Recognition0
Interactive Symbol Grounding with Complex Referential ExpressionsCode0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling0
Few-shot fine-tuning SOTA summarization models for medical dialogues0
AISFG: Abundant Information Slot Filling Generator0
Domain Agnostic Few-shot Learning for Speaker Verification0
Few-Shot Stance Detection via Target-Aware Prompt Distillation0
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding0
Understanding Benign Overfitting in Gradient-Based Meta Learning0
Prompting Decision Transformer for Few-Shot Policy Generalization0
Single Morphing Attack Detection using Siamese Network and Few-shot Learning0
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation LearningCode0
Low Resource Pipeline for Spoken Language Understanding via Weak Supervision0
C^*-algebra Net: A New Approach Generalizing Neural Network Parameters to C^*-algebra0
EEML: Ensemble Embedded Meta-learning0
Lifelong Wandering: A realistic few-shot online continual learning setting0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification0
Language Models are General-Purpose Interfaces0
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams0
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators0
Making Large Language Models Better Reasoners with Step-Aware Verifier0
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-ReptileCode0
The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Online Gesture RecognitionCode0
Guided Deep Metric Learning0
Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code0
Cross-lingual Approaches for the Detection of Adverse Drug Reactions in German from a Patient’s PerspectiveCode0
Turkish Universal Conceptual Cognitive Annotation0
A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis TreatmentCode0
Metaphor Detection for Low Resource Languages: From Zero-Shot to Few-Shot Learning in Middle High GermanCode0
Few-Shot Learning for Argument Aspects of the Nuclear Energy DebateCode0
FHIST: A Benchmark for Few-shot Classification of Histological Images0
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networksCode0
Task-Prior Conditional Variational Auto-Encoder for Few-Shot Image Classification0
Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer0
Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding0
FLEURS: Few-shot Learning Evaluation of Universal Representations of SpeechCode0
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection0
Naive Few-Shot Learning: Uncovering the fluid intelligence of machines0
Adaptive Few-Shot Learning Algorithm for Rare Sound Event Detection0
What Makes Data-to-Text Generation Hard for Pretrained Language Models?0
Self-mentoring: a new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation0
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction0
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning0
Prototypical Calibration for Few-shot Learning of Language ModelsCode0
PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot LearnersCode0
Uncertainty-based Network for Few-shot Image Classification0
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities0
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time AugmentationCode0
EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome0
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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