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 5651–5675 of 10307 papers

TitleStatusHype
A Survey on Deep Tabular Learning—0
SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks—0
A Survey on Deep Industrial Transfer Learning in Fault Prognostics—0
Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning—0
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data—0
SciWING– A Software Toolkit for Scientific Document Processing—0
Forensic Dental Age Estimation Using Modified Deep Learning Neural Network—0
Forest Inspection Dataset for Aerial Semantic Segmentation and Depth Estimation—0
Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis—0
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance—0
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks—0
Forward and Backward Knowledge Transfer for Sentiment Classification—0
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
A Survey on Deep Domain Adaptation for LiDAR Perception—0
Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment—0
A Survey on Computational Intelligence-based Transfer Learning—0
Foundation Model's Embedded Representations May Detect Distribution Shift—0
Foundations of Multivariate Distributional Reinforcement Learning—0
Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence—0
A Survey on Anomaly Detection for Technical Systems using LSTM Networks—0
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning—0
Active Learning for Rumor Identification on Social Media—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