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 8001–8025 of 10307 papers

TitleStatusHype
AI based Safety System for Employees of Manufacturing Industries in Developing Countries—0
Multi-task Optimization Based Co-training for Electricity Consumption Prediction—0
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning—0
Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model—0
Multi-Task Regularization with Covariance Dictionary for Linear Classifiers—0
Multi-Task Reinforcement Learning for Quadrotors—0
AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System—0
Multi-task Representation Learning with Stochastic Linear Bandits—0
Multi-task Self-Supervised Learning for Human Activity Detection—0
Multi-Task Self-Supervised Time-Series Representation Learning—0
Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer 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
AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality—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
SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples—0
Multi-View Priors for Learning Detectors from Sparse Viewpoint Data—0
AI Approaches in Processing and Using Data in Personalized Medicine—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
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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