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 6301–6325 of 10307 papers

TitleStatusHype
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking—0
A Computer Vision Approach to Estimate the Localized Sea State—0
A Probabilistic Approach to Knowledge Translation—0
Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation—0
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks—0
Implicit Surface Contrastive Clustering for LiDAR Point Clouds—0
Imponderous Net for Facial Expression Recognition in the Wild—0
Importance Weighted Adversarial Nets for Partial Domain Adaptation—0
Importance Weighting with a Adversarial Network for Large-Scale Sleep Staging—0
ImpressLearn: Continual Learning via Combined Task Impressions—0
Improved Brain Tumor Detection in MRI: Fuzzy Sigmoid Convolution in Deep Learning—0
Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images—0
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation—0
Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework—0
Improved Cross-Lingual Transfer Learning For Automatic Speech Translation—0
Improved Generalization of Arabic Text Classifiers—0
Improved Knowledge Transfer for Semi-Supervised Domain Adaptation via Trico Training Strategy—0
Efficient Patient Fine-Tuned Seizure Detection with a Tensor Kernel Machine—0
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models—0
Improved pronunciation prediction accuracy using morphology—0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation—0
Self-Transfer Learning for Fully Weakly Supervised Object Localization—0
Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI—0
Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles—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