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 9051–9075 of 10307 papers

TitleStatusHype
Quality In, Quality Out: Learning from Actual Mistakes—0
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing—0
Breast Lump Detection and Localization with a Tactile Glove Using Deep Learning—0
Breast Cancer Image Classification Method Based on Deep Transfer Learning—0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—0
Quality versus Quantity: Building Catalan-English MT Resources—0
ChaLearn LAP Large Scale Signer Independent Isolated Sign Language Recognition Challenge: Design, Results and Future Research—0
Challenges for cognitive decoding using deep learning methods—0
Challenges in including extra-linguistic context in pre-trained language models—0
Training from Zero: Radio Frequency Machine Learning Data Quantity Forecasting—0
Quantifying Knowledge Distillation Using Partial Information Decomposition—0
Change your singer: a transfer learning generative adversarial framework for song to song conversion—0
Channel Scaling: A Scale-and-Select Approach for Transfer Learning—0
Channel-wise pruning of neural networks with tapering resource constraint—0
Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks—0
BreakingNews: Article Annotation by Image and Text Processing—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
Characterizing and Avoiding Negative Transfer—0
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
Switching EEG Headsets Made Easy: Reducing Offline Calibration Effort Using Active Weighted Adaptation Regularization—0
Adaptive transfer learning—0
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
Chest Disease Detection In X-Ray Images Using Deep Learning Classification Method—0
Quantifying the Performance of Federated Transfer Learning—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