30 citations · 105 across the 10 of their papers we have counts for
10 papers
NTK-SAP: Improving neural network pruning by aligning training dynamics
Yite Wang, Dawei Li, Ruoyu Sun
Pruning neural networks before training has received increasing interest due to its potential to reduce training time and memory. One popular method is to prune the connections bas…
RILOD: Near Real-Time Incremental Learning for Object Detection at the Edge
Dawei Li, Serafettin Tasci, Shalini Ghosh +3
Object detection models shipped with camera-equipped edge devices cannot cover the objects of interest for every user. Therefore, the incremental learning capability is a critical…
Regularize, Expand and Compress: Multi-task based Lifelong Learning via NonExpansive AutoML
Jie Zhang, Junting Zhang, Shalini Ghosh +4
Lifelong learning, the problem of continual learning where tasks arrive in sequence, has been lately attracting more attention in the computer vision community. The aim of lifelong…
Class-incremental Learning via Deep Model Consolidation
Junting Zhang, Jie Zhang, Shalini Ghosh +5
Deep neural networks (DNNs) often suffer from "catastrophic forgetting" during incremental learning (IL) --- an abrupt degradation of performance on the original set of classes whe…
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression
Jie Zhang, Xiaolong Wang, Dawei Li +3
State-of-the-art deep model compression methods exploit the low-rank approximation and sparsity pruning to remove redundant parameters from a learned hidden layer. However, they pr…
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices
Jie Zhang, Xiaolong Wang, Dawei Li +1
Recurrent neural networks (RNNs) achieve cutting-edge performance on a variety of problems. However, due to their high computational and memory demands, deploying RNNs on resource…