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 25512600 of 2964 papers

TitleStatusHype
Interpreting and Understanding Graph Convolutional Neural Network using Gradient-based Attribution Method0
Intra-task Mutual Attention based Vision Transformer for Few-Shot Learning0
Invariance-Guided Feature Evolution for Few-Shot Learning0
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning0
Inverse is Better! Fast and Accurate Prompt for Slot Tagging0
Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis0
Investigating grammatical abstraction in language models using few-shot learning of novel noun gender0
Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models0
Investigating Vision-Language Model for Point Cloud-based Vehicle Classification0
IPNET:Influential Prototypical Networks for Few Shot Learning0
Iris: An AI-Driven Virtual Tutor For Computer Science Education0
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks0
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?0
IsOBS: An Information System for Oracle Bone Script0
Is Pre-training Truly Better Than Meta-Learning?0
Is Support Set Diversity Necessary for Meta-Learning?0
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?0
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?0
Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles0
It's About Time: Incorporating Temporality in Retrieval Augmented Language Models0
IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT0
JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset0
Japanese-English Sentence Translation Exercises Dataset for Automatic Grading0
JASMINE: Arabic GPT Models for Few-Shot Learning0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering0
Kernel Methods in Hyperbolic Spaces0
KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting0
KNN Transformer with Pyramid Prompts for Few-Shot Learning0
Knowledge Acquisition on Mass-shooting Events via LLMs for AI-Driven Justice0
Knowledge-augmented Few-shot Visual Relation Detection0
Knowledge Distillation Meets Few-Shot Learning: An Approach for Few-Shot Intent Classification Within and Across Domains0
Knowledge-Driven New Drug Recommendation0
Knowledge-grounded Dialog State Tracking0
Knowledge Guided Metric Learning for Few-Shot Text Classification0
Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition0
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models0
Labeled From Unlabeled: Exploiting Unlabeled Data for Few-Shot Deep HDR Deghosting0
Labeled Memory Networks for Online Model Adaptation0
Label-guided Data Augmentation for Prompt-based Few Shot Learners0
LADs: Leveraging LLMs for AI-Driven DevOps0
Language-guided Few-shot Semantic Segmentation0
Language Models are General-Purpose Interfaces0
Language models are weak learners0
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems0
Large Language Models: A New Approach for Privacy Policy Analysis at Scale0
Large Language Models are Unreliable for Cyber Threat Intelligence0
Large Language Models as Computable Approximations to Solomonoff Induction0
Large Language Models for Patient Comments Multi-Label Classification0
Large Language Models for Solving Economic Dispatch Problem0
Show:102550
← PrevPage 52 of 60Next →

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