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 351400 of 2964 papers

TitleStatusHype
Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with FeedbackCode0
Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English LanguageCode0
Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer ScienceCode0
FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image ClassificationCode0
PALM: Few-Shot Prompt Learning for Audio Language Models0
Model X-Ray: Detection of Hidden Malware in AI Model Weights using Few Shot Learning0
Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samples0
RmGPT: Rotating Machinery Generative Pretrained Model0
A Few Hypocrites: Few-Shot Learning and Subtype Definitions for Detecting Hypocrisy Accusations in Online Climate Change Debates0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting0
Disentangling Questions from Query Generation for Task-Adaptive Retrieval0
PeerArg: Argumentative Peer Review with LLMs0
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularizationCode1
Effectiveness of Cross-linguistic Extraction of Genetic Information using Generative Large Language ModelsCode0
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition0
Hierarchical end-to-end autonomous navigation through few-shot waypoint detection0
Generative AI Is Not Ready for Clinical Use in Patient Education for Lower Back Pain Patients, Even With Retrieval-Augmented Generation0
Privacy Policy Analysis through Prompt Engineering for LLMs0
A Feature Generator for Few-Shot LearningCode0
A Distribution-Aware Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning0
ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language ModelsCode1
E-Sort: Empowering End-to-end Neural Network for Multi-channel Spike Sorting with Transfer Learning and Fast Post-processingCode0
Across-Game Engagement Modelling via Few-Shot Learning0
Are Large Language Models Good Essay Graders?0
Few-Shot Class-Incremental Learning with Non-IID Decentralized Data0
Mixture of Prompt Learning for Vision Language Models0
Few-Shot Learning Approach on Tuberculosis Classification Based on Chest X-Ray Images0
Efficacy of Synthetic Data as a Benchmark0
Reasoning Graph Enhanced Exemplars Retrieval for In-Context LearningCode0
FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing0
Towards Novel Malicious Packet Recognition: A Few-Shot Learning Approach0
LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio TaggingCode0
Frequency-Guided Masking for Enhanced Vision Self-Supervised LearningCode0
Reading ability detection using eye-tracking data with LSTM-based few-shot learningCode0
Music auto-tagging in the long tail: A few-shot approach0
Towards a graph-based foundation model for network traffic analysis0
Efficient and Reliable Vector Similarity Search Using Asymmetric Encoding with NAND-Flash for Many-Class Few-Shot Learning0
ICDAR 2024 Competition on Few-Shot and Many-Shot Layout Segmentation of Ancient Manuscripts (SAM)0
Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo0
Predicting Electricity Consumption with Random Walks on Gaussian Processes0
Improved Visually Prompted Keyword Localisation in Real Low-Resource SettingsCode0
Proto-OOD: Enhancing OOD Object Detection with Prototype Feature Similarity0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning0
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features0
Text-Guided Mixup Towards Long-Tailed Image CategorizationCode0
The representation landscape of few-shot learning and fine-tuning in large language modelsCode0
iText2KG: Incremental Knowledge Graphs Construction Using Large Language ModelsCode4
Benchmarking Spurious Bias in Few-Shot Image ClassifiersCode0
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot LearningCode0
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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