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 7451–7475 of 10307 papers

TitleStatusHype
Transfer-learning-based Surrogate Model for Thermal Conductivity of Nanofluids—0
Transfer Learning Beyond Bounded Density Ratios—0
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners—0
Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection—0
Transfer Learning by Distribution Matching for Targeted Advertising—0
Transfer Learning by Modeling a Distribution over Policies—0
Transfer Learning by Ranking for Weakly Supervised Object Annotation—0
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression—0
Transfer Learning Capabilities of Untrained Neural Networks for MIMO CSI Recreation—0
Transfer learning driven design optimization for inertial confinement fusion—0
Transfer Learning Enhanced Common Spatial Pattern Filtering for Brain Computer Interfaces (BCIs): Overview and a New Approach—0
Transfer Learning Enhanced Full Waveform Inversion—0
Transfer Learning-Enhanced Instantaneous Multi-Person Indoor Localization by CSI—0
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture—0
Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering—0
Transfer learning extensions for the probabilistic classification vector machine—0
Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths—0
Transfer Learning for a Class of Cascade Dynamical Systems—0
Transfer Learning for Action Unit Recognition—0
Transfer Learning for Aided Target Recognition: Comparing Deep Learning to other Machine Learning Approaches—0
Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources—0
Transfer Learning for Algorithm Recommendation—0
Transfer learning for automatic brain tumor classification Using MRI Images.—0
Transfer Learning for Autonomous Chatter Detection in Machining—0
Transfer Learning for Bayesian HPO with End-to-End Meta-Features—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