915 citations · 3.2k across the 107 of their papers we have counts for
9 papers · 1 filter
Exploring Complicated Search Spaces with Interleaving-Free Sampling
Yunjie Tian, Lingxi Xie, Jiemin Fang +3
The existing neural architecture search algorithms are mostly working on search spaces with short-distance connections. We argue that such designs, though safe and stable, obstacle…
General Greedy De-bias Learning
Xinzhe Han, Shuhui Wang, Chi Su +2
Neural networks often make predictions relying on the spurious correlations from the datasets rather than the intrinsic properties of the task of interest, facing sharp degradation…
ESAD: End-to-end Deep Semi-supervised Anomaly Detection
Chaoqin Huang, Fei Ye, Peisen Zhao +3
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new…
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
Xin Chen, Lingxi Xie, Jun Wu +3
Neural architecture search has attracted wide attentions in both academia and industry. To accelerate it, researchers proposed weight-sharing methods which first train a super-netw…
Scalable NAS with Factorizable Architectural Parameters
Lanfei Wang, Lingxi Xie, Tianyi Zhang +2
Neural Architecture Search (NAS) is an emerging topic in machine learning and computer vision. The fundamental ideology of NAS is using an automatic mechanism to replace manual des…
Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters
Kaifeng Bi, Changping Hu, Lingxi Xie +3
DARTS is a popular algorithm for neural architecture search (NAS). Despite its great advantage in search efficiency, DARTS often suffers weak stability, which reflects in the large…
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data
Zhuoxun He, Lingxi Xie, Xin Chen +3
Data augmentation has been widely applied as an effective methodology to improve generalization in particular when training deep neural networks. Recently, researchers proposed a f…
Defending Adversarial Attacks by Correcting logits
Yifeng Li, Lingxi Xie, Ya Zhang +3
Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning. While previous research verified that adversarial attacks are often frag…
Accelerate CNN via Recursive Bayesian Pruning
Yuefu Zhou, Ya Zhang, Yanfeng Wang +1
Channel Pruning, widely used for accelerating Convolutional Neural Networks, is an NP-hard problem due to the inter-layer dependency of channel redundancy. Existing methods general…