SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 9401–9425 of 10307 papers

TitleStatusHype
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference ModelCode0
Feudal Graph Reinforcement LearningCode0
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learningCode0
Few-shot classification in Named Entity Recognition TaskCode0
Few-Shot Fruit Segmentation via Transfer LearningCode0
Few-Shot Image Recognition With Knowledge TransferCode0
Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain StudyCode0
Few-Shot Learning for Image Classification of Common FloraCode0
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant SetupCode0
Few-Shot Transfer Learning to improve Chest X-Ray pathology detection using limited tripletsCode0
A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service InferenceCode0
Fine-Grained Classification for Poisonous Fungi Identification with Transfer LearningCode0
Fine-Grained Emotion Prediction by Modeling Emotion DefinitionsCode0
Fine-grained Sentiment Classification using BERTCode0
Finger Pose Estimation for Under-screen Fingerprint SensorCode0
First-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher NetworkCode0
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image AnalysisCode0
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer LearningCode0
Flat Posterior Does Matter For Bayesian Model AveragingCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Flexible Option LearningCode0
FM-OV3D: Foundation Model-based Cross-modal Knowledge Blending for Open-Vocabulary 3D DetectionCode0
Focus on the Positives: Self-Supervised Learning for Biodiversity MonitoringCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment AnalysisCode0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified