Search Results for author: Chris Ding

Found 23 papers, 5 papers with code

Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection

1 code implementation13 Aug 2023 Wenwen Min, Taosheng Xu, Chris Ding

However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples.

Dimensionality Reduction feature selection

X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models

1 code implementation18 May 2023 Yixiong Chen, Li Liu, Chris Ding

This paper introduces a novel explainable image quality evaluation approach called X-IQE, which leverages visual large language models (LLMs) to evaluate text-to-image generation methods by generating textual explanations.

Benchmarking Text-to-Image Generation

Rethinking Two Consensuses of the Transferability in Deep Learning

no code implementations1 Dec 2022 Yixiong Chen, Jingxian Li, Chris Ding, Li Liu

Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans.

General Knowledge Image Classification +2

Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning

1 code implementation31 Oct 2022 Longkang Li, Siyuan Liang, Zihao Zhu, Chris Ding, Hongyuan Zha, Baoyuan Wu

Compared to the state-of-the-art reinforcement learning method, our model's network parameters are reduced to only 37\% of theirs, and the solution gap of our model towards the expert solutions decreases from 6. 8\% to 1. 3\% on average.

Computational Efficiency Imitation Learning +3

MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging

1 code implementation3 Jun 2022 Yixiong Chen, Li Liu, Jingxian Li, Hua Jiang, Chris Ding, Zongwei Zhou

In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains.

Meta-Learning Transfer Learning

Neuron-Enhanced Autoencoder based Collaborative filtering: Theory and Practice

no code implementations29 Sep 2021 Jicong Fan, Rui Chen, Chris Ding

We provide theoretical analysis for NE-AECF to investigate the generalization ability of autoencoder and deep learning in collaborative filtering.

Collaborative Filtering

Learning Class Unique Features in Fine-Grained Visual Classification

no code implementations22 Nov 2020 Runkai Zheng, Zhijia Yu, Yinqi Zhang, Chris Ding, Hei Victor Cheng, Li Liu

A major challenge in Fine-Grained Visual Classification (FGVC) is distinguishing various categories with high inter-class similarity by learning the feature that differentiate the details.

Classification Fine-Grained Image Classification +1

Transductive Semi-Supervised Deep Learning using Min-Max Features

no code implementations ECCV 2018 Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, Nanning Zheng

In this paper, we propose Transductive Semi-Supervised Deep Learning (TSSDL) method that is effective for training Deep Convolutional Neural Network (DCNN) models.

General Classification Image Classification +1

Minimal Support Vector Machine

no code implementations6 Apr 2018 Shuai Zheng, Chris Ding

Support Vector Machine (SVM) is an efficient classification approach, which finds a hyperplane to separate data from different classes.

Classification General Classification

Graph Matching via Multiplicative Update Algorithm

no code implementations NeurIPS 2017 Bo Jiang, Jin Tang, Chris Ding, Yihong Gong, Bin Luo

As a fundamental problem in computer vision, graph matching problem can usually be formulated as a Quadratic Programming (QP) problem with doubly stochastic and discrete (integer) constraints.

Graph Matching

Binary Constraint Preserving Graph Matching

no code implementations CVPR 2017 Bo Jiang, Jin Tang, Chris Ding, Bin Luo

There are three main contributions of the proposed method: (1) we propose a new graph matching relaxation model, called Binary Constraint Preserving Graph Matching (BPGM), which aims to incorporate the discrete binary mapping constraints more in graph matching relaxation.

Graph Matching

Revisiting L21-norm Robustness with Vector Outlier Regularization

no code implementations20 Jun 2017 Bo Jiang, Chris Ding

One interesting property of VOR is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results.

L1-norm Error Function Robustness and Outlier Regularization

no code implementations28 May 2017 Chris Ding, Bo Jiang

(1) A key property of outlier regularization is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results.

Multiple Images Recovery Using a Single Affine Transformation

no code implementations23 May 2017 Bo Jiang, Chris Ding, Bin Luo

One approach to deal with noise image data is to use data recovery techniques which aim to recover the true uncorrupted signals from the observed noise images.

Kernel Alignment Inspired Linear Discriminant Analysis

no code implementations14 Oct 2016 Shuai Zheng, Chris Ding

The problem is to find a subspace to maximize the alignment between subspace-transformed data kernel and class indicator kernel.

A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification

no code implementations14 Oct 2016 Shuai Zheng, Feiping Nie, Chris Ding, Heng Huang

In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix.

Classification General Classification +2

Heterogeneous Visual Features Fusion via Sparse Multimodal Machine

no code implementations CVPR 2013 Hua Wang, Feiping Nie, Heng Huang, Chris Ding

We applied our SMML method to five broadly used object categorization and scene understanding image data sets for both singlelabel and multi-label image classification tasks.

Feature Importance Multi-Label Image Classification +2

Symmetric Nonnegative Matrix Factorization for Graph Clustering

1 code implementation SDM 2012 Da Kuang, Chris Ding, Haesun Park

Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).

Clustering Graph Clustering +1

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