# Deep Transfer Learning for Intelligent Vehicle Perception: a Survey arXiv: https://arxiv.org/abs/2306.15110 ยท HTML: https://mlsanity.com/paper/2306.15110/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 115 of its 187 references that are in the map. - First posted on arXiv 26 Jun 2023. ## Builds on (most cited first, 30 of 115) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016) - [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019) - [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016) - [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019) - [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015) - [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014) - [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018) - [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020) - [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015) - [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017) - [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019) - [CyCADA: Cycle-Consistent Adversarial Domain Adaptation](https://mlsanity.com/paper/1711.03213/) (2017) - [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016) - [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016) - [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021) - [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021) - [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020) - [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017) - [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016) - [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017) - [KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D](https://mlsanity.com/paper/2109.13410/) (2021) - [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018) - [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021) - [Semantic Foggy Scene Understanding with Synthetic Data](https://mlsanity.com/paper/1708.07819/) (2017) - [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019) - [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019) - [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018) ## Built on by (most cited first, 10 of 10) - [PhysGen3D: Crafting a Miniature Interactive World from a Single Image](https://mlsanity.com/paper/2503.20746/) (2025) - [EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV](https://mlsanity.com/paper/2403.05422/) (2024) - [VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification](https://mlsanity.com/paper/2311.16278/) (2023) - [Stealthy Multi-Task Adversarial Attacks](https://mlsanity.com/paper/2411.17936/) (2024) - [HiLO: High-Level Object Fusion for Autonomous Driving using Transformers](https://mlsanity.com/paper/2506.02554/) (2025) - [High-Fidelity Digital Twins for Bridging the Sim2Real Gap in LiDAR-Based ITS Perception](https://mlsanity.com/paper/2509.02904/) (2025) - [BMD-45: A Large-Scale CCTV Vehicle Detection Dataset for Urban Traffic in Developing Cities](https://mlsanity.com/paper/2604.24419/) (2026) - [An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers](https://mlsanity.com/paper/2606.05149/) (2026) - [Instance-Aware Knowledge Distillation for Semi-Supervised Learning of an On-Board Multi-Task Dense Prediction Model for Collision Avoidance System](https://mlsanity.com/paper/2606.16414/) (2026) - [A Stitch in Time Saves Nine: Preserving Policy Compatibility Under Perception Updates in End-to-End Autonomous Driving](https://mlsanity.com/paper/2606.21509/) (2026) ## Abstract (arXiv) Deep learning-based intelligent vehicle perception has been developing prominently in recent years to provide a reliable source for motion planning and decision making in autonomous driving. A large number of powerful deep learning-based methods can achieve excellent performance in solving various perception problems of autonomous driving. However, these deep learning methods still have several limitations, for example, the assumption that lab-training (source domain) and real-testing (target domain) data follow the same feature distribution may not be practical in the real world. There is often a dramatic domain gap between them in many real-world cases. As a solution to this challenge, deep transfer learning can handle situations excellently by transferring the knowledge from one domain to another. Deep transfer learning aims to improve task performance in a new domain by leveraging the knowledge of similar tasks learned in another domain before. Nevertheless, there are currently no survey papers on the topic of deep transfer learning for intelligent vehicle perception. To the best of our knowledge, this paper represents the first comprehensive survey on the topic of the deep transfer learning for intelligent vehicle perception. This paper discusses the domain gaps related to the differences of sensor, data, and model for the intelligent vehicle perception. The recent applications, challenges, future researches in intelligent vehicle perception are also explored.