13 citations · 31 across the 55 of their papers we have counts for
6 papers · 1 filter
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
Jinggang Chen, Junjie Li, Xiaoyang Qu +3
Detecting out-of-distribution (OOD) examples is crucial to guarantee the reliability and safety of deep neural networks in real-world settings. In this paper, we offer an innovativ…
EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
Liang Wang, Nan Zhang, Xiaoyang Qu +6
Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewe…
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer
Chenghao Liu, Xiaoyang Qu, Jianzong Wang +1
Federated Learning (FL) has been widely concerned for it enables decentralized learning while ensuring data privacy. However, most existing methods unrealistically assume that the…
Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning
Liang Wang, Kai Lu, Nan Zhang +5
This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes. Shoggoth uses online kno…
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing
Jinggang Chen, Xiaoyang Qu, Junjie Li +3
Out-of-distribution (OOD) detection aims at enhancing standard deep neural networks to distinguish anomalous inputs from original training data. Previous progress has introduced va…
Feature-Rich Audio Model Inversion for Data-Free Knowledge Distillation Towards General Sound Classification
Zuheng Kang, Yayun He, Jianzong Wang +3
Data-Free Knowledge Distillation (DFKD) has recently attracted growing attention in the academic community, especially with major breakthroughs in computer vision. Despite promisin…