14 code implementations • 3 Jan 2021 • Jing Xu, Yu Pan, Xinglin Pan, Steven Hoi, Zhang Yi, Zenglin Xu
The ResNet and its variants have achieved remarkable successes in various computer vision tasks.
Ranked #3 on Medical Image Classification on NCT-CRC-HE-100K
no code implementations • 24 Feb 2018 • Yanan Sun, Gary G. Yen, Zhang Yi
Inverted Generational Distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multi- and many-objective evolutionary algorithms.
no code implementations • 24 Feb 2018 • Yanan Sun, Gary G. Yen, Zhang Yi
Finally, by assigning the Pareto-optimal solutions to the uniformly distributed reference vectors, a set of solutions with excellent diversity and convergence is obtained.
no code implementations • 13 Dec 2017 • Yanan Sun, Gary G. Yen, Zhang Yi
Specifically, error classification rate on MNIST with $1. 15\%$ is reached by the proposed algorithm consistently, which is a very promising result against state-of-the-art unsupervised DL algorithms.
no code implementations • 25 Sep 2017 • Xi Peng, Jiashi Feng, Shijie Xiao, Jiwen Lu, Zhang Yi, Shuicheng Yan
In this paper, we present a deep extension of Sparse Subspace Clustering, termed Deep Sparse Subspace Clustering (DSSC).
no code implementations • 24 Apr 2013 • Liangli Zhen, Zhang Yi, Xi Peng, Dezhong Peng
There are two popular schemes to construct a similarity graph, i. e., pairwise distance based scheme and linear representation based scheme.
no code implementations • 7 Mar 2014 • Jie Chen, Hua Mao, Yongsheng Sang, Zhang Yi
In this paper, we propose a low-rank representation with symmetric constraint (LRRSC) method for robust subspace clustering.
no code implementations • 17 Nov 2014 • Xi Peng, Jiwen Lu, Zhang Yi, Rui Yan
In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i. e., automatic subspace learning), and 2) how to learn the underlying subspace in the presence of Gaussian noise (i. e., robust subspace learning).
no code implementations • 26 Feb 2015 • Xi Peng, Can-Yi Lu, Zhang Yi, Huajin Tang
A lot of works have shown that frobenius-norm based representation (FNR) is competitive to sparse representation and nuclear-norm based representation (NNR) in numerous tasks such as subspace clustering.
no code implementations • 25 Sep 2013 • Xi Peng, Huajin Tang, Lei Zhang, Zhang Yi, Shijie Xiao
In this paper, we propose a unified framework which makes representation-based subspace clustering algorithms feasible to cluster both out-of-sample and large-scale data.
no code implementations • 22 Sep 2014 • Xi Peng, Rui Yan, Bo Zhao, Huajin Tang, Zhang Yi
Although the methods achieve a higher recognition rate than the traditional SPM, they consume more time to encode the local descriptors extracted from the image.
no code implementations • 31 Oct 2014 • Jie Chen, Haixian Zhang, Hua Mao, Yongsheng Sang, Zhang Yi
We propose a symmetric low-rank representation (SLRR) method for subspace clustering, which assumes that a data set is approximately drawn from the union of multiple subspaces.
no code implementations • 5 Sep 2012 • Xi Peng, Zhiding Yu, Huajin Tang, Zhang Yi
Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i. e., intra-subspace data points).
no code implementations • 4 Oct 2012 • Xi Peng, Lei Zhang, Zhang Yi, Kok Kiong Tan
The model of low-dimensional manifold and sparse representation are two well-known concise models that suggest each data can be described by a few characteristics.
no code implementations • CVPR 2013 • Xi Peng, Lei Zhang, Zhang Yi
To address the problems, this paper proposes out-of-sample extension of SSC, named as Scalable Sparse Subspace Clustering (SSSC), which makes SSC feasible to cluster large scale data sets.
no code implementations • 29 Nov 2015 • Zhang Yi, Xiao Yanghua, Hwang Seung-won, Wang Wei
However, as such increase of recall often invites false positives and decreases precision in return, we propose the following two techniques: First, we identify concepts with different relatedness to generate linear orderings and pairwise ordering constraints.
no code implementations • 29 Sep 2021 • Liu Zhi, Xiaojie Guo, Zhang Yi
Semantic segmentation aims to map each pixel of an image into its correspond-ing semantic label.
no code implementations • 14 Jul 2022 • Xia Yuan, Jianping Gou, Baosheng Yu, Jiali Yu, Zhang Yi
Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage.