FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection

• URL: http://arxiv.org/abs/2409.07839v1
• Date: Thu, 12 Sep 2024 08:38:42 GMT
• Title: FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection
• Authors: Xinying Lu, Jianli Xiao,
• Abstract summary: This paper proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For traffic incident detection, the acquisition of data and labels is notably resource-intensive, rendering semi-supervised traffic incident detection both a formidable and consequential challenge. Thus, this paper focuses on traffic incident detection with a semi-supervised learning way. It proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. During the mix-up process in the hidden space, it employs a probabilistic pseudo-mixing mechanism to enhance regularization and elevate model precision. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training. Through empirical validation on four authentic datasets, our FPMT model exhibits outstanding performance across various metrics. Particularly noteworthy is its robust performance even in scenarios with low label rates.

Related papers

• Modeling IoT Traffic Patterns: Insights from a Statistical Analysis of an MTC Dataset [1.2289361708127877]
  Internet-of-Things (IoT) is rapidly expanding, connecting numerous devices and becoming integral to our daily lives. Effective IoT traffic management requires modeling and predicting intrincate machine-type communication (MTC) dynamics. We perform a comprehensive statistical analysis of the MTC traffic utilizing goodness-of-fit tests, including well-established tests such as Kolmogorov-Smirnov, Anderson-Darling, chi-squared, and root mean square error.
  arXiv  Detail & Related papers  (2024-09-03T14:24:18Z)
• TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models [27.306180426294784]
  We introduce TPLLM, a novel traffic prediction framework leveraging Large Language Models (LLMs) In this framework, we construct a sequence embedding layer based on Conal Neural Networks (LoCNNs) and a graph embedding layer based on Graph Contemporalal Networks (GCNs) to extract sequence features and spatial features. Experiments on two real-world datasets demonstrate commendable performance in both full-sample and few-shot prediction scenarios.
  arXiv  Detail & Related papers  (2024-03-04T17:08:57Z)
• AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning [98.26836657967162]
  textbfAgentOhana aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. textbfxLAM-v0.1, a large action model tailored for AI agents, demonstrates exceptional performance across various benchmarks.
  arXiv  Detail & Related papers  (2024-02-23T18:56:26Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics [24.397029434125873]
  We develop an unsupervised method to detect co-occurring salient objects in an image group. For the first time, we show that a large unlabeled dataset e.g. ImageNet-1k can be effectively leveraged to improve unsupervised CoSOD performance. To avoid propagating erroneous signals from predictions on unlabeled data, we propose a confidence estimation module.
  arXiv  Detail & Related papers  (2023-11-11T19:47:16Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model [38.722096508198106]
  We present a SEMantic Masked recurrent world model (SEM2), which introduces a semantic filter to extract key driving-relevant features and make decisions via the filtered features. Our method outperforms the state-of-the-art approaches in terms of sample efficiency and robustness to input permutations.
  arXiv  Detail & Related papers  (2022-10-08T13:00:08Z)
• Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency [19.97236038722335]
  Supervised learning has been widely used for attack detection, which requires large amounts of high-quality data and labels. We propose a semi-supervised fine-grained attack categorization framework consisting of an encoder and a two-branch structure. We show that our model outperforms the state-of-the-art semi-supervised attack detection methods with a general 5% improvement in classification accuracy and a 90% reduction in training time.
  arXiv  Detail & Related papers  (2022-05-19T16:30:31Z)
• Robust Event Classification Using Imperfect Real-world PMU Data [58.26737360525643]
  We study robust event classification using imperfect real-world phasor measurement unit (PMU) data. We develop a novel machine learning framework for training robust event classifiers.
  arXiv  Detail & Related papers  (2021-10-19T17:41:43Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.