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 7676–7700 of 10307 papers

TitleStatusHype
Tiered Graph Autoencoders with PyTorch Geometric for Molecular Graphs—0
Tight Rates in Supervised Outlier Transfer Learning—0
Time-Frequency Analysis based Blind Modulation Classification for Multiple-Antenna Systems—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance—0
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources—0
Time-Variant Variational Transfer for Value Functions—0
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters—0
TinyFedTL: Federated Transfer Learning on Tiny Devices—0
TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation—0
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT—0
Tissue Segmentation of Thick-Slice Fetal Brain MR Scans with Guidance from High-Quality Isotropic Volumes—0
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks—0
TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning—0
Transmission Line Defect Detection Based on UAV Patrol Images and Vision-language Pretraining—0
TLETA: Deep Transfer Learning and Integrated Cellular Knowledge for Estimated Time of Arrival Prediction—0
TLMCM Network for Medical Image Hierarchical Multi-Label Classification—0
TL-PCA: Transfer Learning of Principal Component Analysis—0
TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples—0
TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection—0
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis—0
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents—0
Tool and Phase recognition using contextual CNN features—0
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks—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