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 8201–8225 of 10307 papers

TitleStatusHype
Multilinear Compressive Learning with Prior KnowledgeCode0
Patient-Specific Finetuning of Deep Learning Models for Adaptive Radiotherapy in Prostate CT—0
CRL: Class Representative Learning for Image Classification—0
The Utility of General Domain Transfer Learning for Medical Language Tasks—0
Automatic lesion segmentation and Pathological Myopia classification in fundus images—0
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection—0
A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the WildCode0
On the Value of Target Data in Transfer Learning—0
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System—0
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space—0
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals—0
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition—0
Weighted Empirical Risk Minimization: Sample Selection Bias Correction based on Importance Sampling—0
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer LearningCode0
On transfer learning of neural networks using bi-fidelity data for uncertainty propagation—0
Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics—0
Convolutional Neural Networks and a Transfer Learning Strategy to Classify Parkinson's Disease from Speech in Three Different Languages—0
2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small dataCode0
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration—0
Localized Flood DetectionWith Minimal Labeled Social Media Data Using Transfer Learning—0
Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection—0
GradMix: Multi-source Transfer across Domains and Tasks—0
Unlabeled Data Deployment for Classification of Diabetic Retinopathy Images Using Knowledge Transfer—0
CIFAR-10 Image Classification Using Feature EnsemblesCode0
Student/Teacher Advising through Reward Augmentation—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