Accelerating DNN execution on resource-limited computing platforms has been a long-standing problem. Prior works utilize ℓ1-based group lasso or dynamic regularization such as ADMM to perform structured pruning on DNN models to leverage the parallel …
Object detection plays an important role in self-driving cars for security development. However, mobile systems on self-driving cars with limited computation resources lead to difficulties for object detection. To facilitate this, we propose a …
This paper proposes to use intrinsic examples as a DNN fingerprinting technique for the functionality verification of DNN models implemented on edge devices. The proposed intrinsic examples do not affect the normal DNN training and can enable the …
Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption …
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models. This is the first work taking …
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network …
In autonomous driving, 3D object detection is es-sential as it provides basic knowledge about the environment. However, as deep learning based 3D detection methods are usually computation intensive, it is challenging to support realtime 3D object …
High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications. However, the constrained computation and storage resources on these devices still pose significant challenges for …
There have been many recent attempts to extend the successes of convolutional neural networks (CNNs) from 2-dimensional (2D) image classification to 3-dimensional (3D) video recognition by exploring 3D CNNs. Considering the emerging growth of mobile …
Mode connectivity provides novel geometric insights on analyzing loss landscapes and enables building high-accuracy pathways between well-trained neural networks. In this work, we propose to employ mode connectivity in loss landscapes to study the …