39 citations · 77 across the 10 of their papers we have counts for
10 papers
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification
Yi Liao, Yongsheng Gao, Weichuan Zhang
Decisions made by convolutional neural networks(CNN) can be understood and explained by visualizing discriminative regions on images. To this end, Class Activation Map (CAM) based…
Experts in the Loop: Conditional Variable Selection for Accelerating Post-Silicon Analysis Based on Deep Learning
Yiwen Liao, Raphaël Latty, Bin Yang
Post-silicon validation is one of the most critical processes in modern semiconductor manufacturing. Specifically, correct and deep understanding in test cases of manufactured devi…
Deep Feature Selection Using a Novel Complementary Feature Mask
Yiwen Liao, Jochen Rivoir, Raphaël Latty +1
Feature selection has drawn much attention over the last decades in machine learning because it can reduce data dimensionality while maintaining the original physical meaning of fe…
A Deep-Learning-Aided Pipeline for Efficient Post-Silicon Tuning
Yiwen Liao, Bin Yang, Raphaël Latty +1
In post-silicon validation, tuning is to find the values for the tuning knobs, potentially as a function of process parameters and/or known operating conditions. In this sense, an…
Conditional Variable Selection for Intelligent Test
Yiwen Liao, Tianjie Ge, Raphaël Latty +1
Intelligent test requires efficient and effective analysis of high-dimensional data in a large scale. Traditionally, the analysis is often conducted by human experts, but it is not…
Anomaly Detection Based on Selection and Weighting in Latent Space
Yiwen Liao, Alexander Bartler, Bin Yang
With the high requirements of automation in the era of Industry 4.0, anomaly detection plays an increasingly important role in higher safety and reliability in the production and m…
Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization
Yiwen Liao, Raphaël Latty, Bin Yang
Feature selection is generally used as one of the most important preprocessing techniques in machine learning, as it helps to reduce the dimensionality of data and assists research…
Open-Set Recognition Using Intra-Class Splitting
Patrick Schlachter, Yiwen Liao, Bin Yang
This paper proposes a method to use deep neural networks as end-to-end open-set classifiers. It is based on intra-class data splitting. In open-set recognition, only samples from a…
Deep One-Class Classification Using Intra-Class Splitting
Patrick Schlachter, Yiwen Liao, Bin Yang
This paper introduces a generic method which enables to use conventional deep neural networks as end-to-end one-class classifiers. The method is based on splitting given data from…
One-Class Feature Learning Using Intra-Class Splitting
Patrick Schlachter, Yiwen Liao, Bin Yang
This paper proposes a novel generic one-class feature learning method based on intra-class splitting. In one-class classification, feature learning is challenging, because only sam…