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 7926–7950 of 10307 papers

TitleStatusHype
Multiple-Exit Tuning: Towards Inference-Efficient Adaptation for Vision Transformer—0
A Knowledge Transfer Framework for Differentially Private Sparse Learning—0
A Knowledge-Grounded Dialog System Based on Pre-Trained Language Models—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
Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization—0
Multi-Relevance Transfer Learning—0
A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks—0
A Joint Multiple Criteria Model in Transfer Learning for Cross-domain Chinese Word Segmentation—0
A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks—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
Speech-Image Semantic Alignment Does Not Depend on Any Prior Classification Tasks—0
Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
Multi-Scale Weight Sharing Network for Image Recognition—0
Multi-Scenario Ranking with Adaptive Feature Learning—0
MultiSlav: Using Cross-Lingual Knowledge Transfer to Combat the Curse of Multilinguality—0
Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity—0
Multi-source adversarial transfer learning based on similar source domains with local features—0
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network—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