661 citations · 2k across the 35 of their papers we have counts for
7 papers · 1 filter
Global Vision Transformer Pruning with Hessian-Aware Saliency
Huanrui Yang, Hongxu Yin, Maying Shen +3
Transformers yield state-of-the-art results across many tasks. However, their heuristically designed architecture impose huge computational costs during inference. This work aims o…
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack
Jingyang Zhang, Hsin-Pai Cheng, Chunpeng Wu +2
In recent years, deep neural networks demonstrated state-of-the-art performance in a large variety of tasks and therefore have been adopted in many applications. On the other hand,…
Differentiable Fine-grained Quantization for Deep Neural Network Compression
Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo +4
Neural networks have shown great performance in cognitive tasks. When deploying network models on mobile devices with limited resources, weight quantization has been widely adopted…
DPatch: An Adversarial Patch Attack on Object Detectors
Xin Liu, Huanrui Yang, Ziwei Liu +3
Object detectors have emerged as an indispensable module in modern computer vision systems. In this work, we propose DPatch -- a black-box adversarial-patch-based attack towards ma…
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
Chunpeng Wu, Wei Wen, Tariq Afzal +3
Recently, DNN model compression based on network architecture design, e.g., SqueezeNet, attracted a lot attention. No accuracy drop on image classification is observed on these ext…
Coordinating Filters for Faster Deep Neural Networks
Wei Wen, Cong Xu, Chunpeng Wu +3
Very large-scale Deep Neural Networks (DNNs) have achieved remarkable successes in a large variety of computer vision tasks. However, the high computation intensity of DNNs makes i…
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Jongsoo Park, Sheng Li, Wei Wen +4
Phenomenally successful in practical inference problems, convolutional neural networks (CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The numbe…