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
Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia—0
AgentPose: Progressive Distribution Alignment via Feature Agent for Human Pose Distillation—0
Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization—0
NRC-CNRC Systems for Upper Sorbian-German and Lower Sorbian-German Machine Translation 2021—0
NRC Systems for Low Resource German-Upper Sorbian Machine Translation 2020: Transfer Learning with Lexical Modifications—0
AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes—0
AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation—0
NSCGCN: A novel deep GCN model to diagnosis COVID-19—0
Statistical Deficiency for Task Inclusion Estimation—0
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction—0
NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning—0
Statistical Hardware Design With Multi-model Active Learning—0
Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies—0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
A General Regularization Framework for Domain Adaptation—0
NUIG-DSI at the WebNLG+ challenge: Leveraging Transfer Learning for RDF-to-text generation—0
NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains—0
NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers—0
Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks—0
NUMSnet: Nested-U Multi-class Segmentation network for 3D Medical Image Stacks—0
Nurse-in-the-Loop Artificial Intelligence for Precision Management of Type 2 Diabetes in a Clinical Trial Utilizing Transfer-Learned Predictive Digital Twin—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels—0
Object detection-based inspection of power line insulators: Incipient fault detection in the low data-regime—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