scholar.google.com › citations
Driving behavior recognition usually uses time series data, while transfer learning mainly involves two kinds of tasks in time series analysis: (1) sequence prediction (including classification and regression) and (2) anomaly detection.
Mar 1, 2023
Aug 23, 2019 · TLDPR transfers knowledge from labeled samples in source domain to identify the patterns of samples in target domain without label. A domain is ...
Discriminative Transfer Learning for Driving Pattern Recognition in ...
pubmed.ncbi.nlm.nih.gov › ...
To handle the scarcity of labeled data, we have developed a novel discriminative transfer learning method for driving pattern recognition to leverage knowledge ...
In this paper, to tackle this challenging recognition task, we propose a novel and robust Transfer Learning method for Driving Pattern Recognition (TLDPR) that ...
In this paper, to tackle this challenging recognition task, we propose a novel and robust Transfer Learning method for Driving Pattern Recognition (TLDPR) that ...
Mar 14, 2022 · 1) DTL develops a novel transfer learning technique to address the driving pattern recognition problem for unlabeled scenes. 2) The proposed ...
Jun 26, 2023 · Deep transfer learning aims to improve task performance in a new domain by leveraging the knowledge of similar tasks learned in another domain ...
Nov 15, 2022 · We propose a transfer learning framework for driving style classification in which we use a previously developed rule-based algorithm.
In this paper, to tackle this challenging recognition task, we propose a novel and robust Transfer Learning method for Driving Pattern Recognition (TLDPR) that ...
Jul 23, 2020 · We show that transfer learning on simulated data sets provide better generalization and collision avoidance, as compared to random initialization methods.