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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4501–4525 of 10307 papers

TitleStatusHype
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Multi-Task Supervised Pretraining for Neural Domain Adaptation—0
Multi-task transfer learning for finding actionable information from crisis-related messages on social media—0
Multi-Transfer Learning Techniques for Detecting Auditory Brainstem Response—0
Multivariate and Online Transfer Learning with Uncertainty Quantification—0
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
Multiview Contrastive Learning for Unsupervised Domain Adaptation in Brain–Computer Interfaces—0
Multi-View Cross-Lingual Structured Prediction with Minimum Supervision—0
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data—0
Multi-way VNMT for UGC: Improving Robustness and Capacity via Mixture Density Networks—0
MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid—0
muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems—0
Music auto-tagging in the long tail: A few-shot approach—0
Mutual Alignment Transfer Learning—0
Mutual Clustering on Comparative Texts via Heterogeneous Information Networks—0
Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer—0
Mutual Information-guided Knowledge Transfer for Novel Class Discovery—0
Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization—0
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning—0
Mutual Transfer Learning for Massive Data—0
MV2MAE: Multi-View Video Masked Autoencoders—0
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation—0
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models—0
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning—0
Show:102550
← PrevPage 181 of 413Next →
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