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 4426–4450 of 10307 papers

TitleStatusHype
Multimodal Transfer Learning-based Approaches for Retinal Vascular Segmentation—0
Multi-modal Transfer Learning between Biological Foundation Models—0
Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)—0
Multi-Module Recurrent Neural Networks with Transfer Learning—0
Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustness—0
Multi-objective Neural Architecture Search with Almost No Training—0
MultIOD: Rehearsal-free Multihead Incremental Object Detector—0
Multi-Organ Cancer Classification and Survival Analysis—0
Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation—0
Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network—0
Multi-path Neural Networks for On-device Multi-domain Visual Classification—0
Multiphase flow prediction with deep neural networks—0
Multi-Platform Methane Plume Detection via Model and Domain Adaptation—0
Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer—0
Multiple Pivot Languages and Strategic Decoder Initialization Helps Neural Machine Translation—0
Multiple Yield Curve Modeling and Forecasting using Deep Learning—0
Multiply Robust Federated Estimation of Targeted Average Treatment Effects—0
Multipurpose Intelligent Process Automation via Conversational Assistant—0
Multi-Relevance Transfer Learning—0
Multi-Robot Transfer Learning: A Dynamical System Perspective—0
Multiscale Color Guided Attention Ensemble Classifier for Age-Related Macular Degeneration using Concurrent Fundus and Optical Coherence Tomography Images—0
Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks—0
Multi scale Feature Extraction and Fusion for Online Knowledge Distillation—0
Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning—0
Multi-Scale Weight Sharing Network for Image Recognition—0
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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