5 citations · 14 across the 9 of their papers we have counts for
10 papers
ODG-Q: Robust Quantization via Online Domain Generalization
Chaofan Tao, Ngai Wong
Quantizing neural networks to low-bitwidth is important for model deployment on resource-limited edge hardware. Although a quantized network has a smaller model size and memory foo…
MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator
Chang Liu, Xiaoyan Qian, Xiaojuan Qi +3
Manually annotating 3D point clouds is laborious and costly, limiting the training data preparation for deep learning in real-world object detection. While a few previous studies t…
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
Rui Lin, Jie Ran, King Hung Chiu +2
We introduce a new kind of linear transform named Deformable Butterfly (DeBut) that generalizes the conventional butterfly matrices and can be adapted to various input-output dimen…
What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study
Binxiao Huang, Chaofan Tao, Rui Lin +1
Although many fields have witnessed the superior performance brought about by deep learning, the robustness of neural networks remains an open issue. Specifically, a small adversar…
EZCrop: Energy-Zoned Channels for Robust Output Pruning
Rui Lin, Jie Ran, Dongpeng Wang +2
Recent results have revealed an interesting observation in a trained convolutional neural network (CNN), namely, the rank of a feature map channel matrix remains surprisingly const…
AET-EFN: A Versatile Design for Static and Dynamic Event-Based Vision
Chang Liu, Xiaojuan Qi, Edmund Lam +1
The neuromorphic event cameras, which capture the optical changes of a scene, have drawn increasing attention due to their high speed and low power consumption. However, the event…