Search Results for author: Chenglong Bao

Found 29 papers, 11 papers with code

Interpolation between CNNs and ResNets

no code implementations ICML 2020 Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi

Although ordinary differential equations (ODEs) provide insights for designing networks architectures, its relationship with the non-residual convolutional neural networks (CNNs) is still unclear.

Adversarial Attack Image Classification

SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder

1 code implementation26 Mar 2024 Dihan Zheng, Yihang Zou, Xiaowen Zhang, Chenglong Bao

We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks.

Image Denoising Image Restoration +2

Convection-Diffusion Equation: A Theoretically Certified Framework for Neural Networks

no code implementations23 Mar 2024 Tangjun Wang, Chenglong Bao, Zuoqiang Shi

Neural network can be viewed as a map from a simple base model to a complicate function.

Reconstruction of dynamical systems from data without time labels

no code implementations7 Dec 2023 Zhijun Zeng, Pipi Hu, Chenglong Bao, Yi Zhu, Zuoqiang Shi

In this paper, we study the method to reconstruct dynamical systems from data without time labels.

Addressing preferred orientation in single-particle cryo-EM through AI-generated auxiliary particles

no code implementations26 Sep 2023 HUI ZHANG, Dihan Zheng, Qiurong Wu, Nieng Yan, Zuoqiang Shi, Mingxu Hu, Chenglong Bao

The single-particle cryo-EM field faces the persistent challenge of preferred orientation, lacking general computational solutions.

Single Particle Analysis

An axiomatized PDE model of deep neural networks

no code implementations23 Jul 2023 Tangjun Wang, Wenqi Tao, Chenglong Bao, Zuoqiang Shi

Based on the convection-diffusion equation, we design a new training method for ResNets.

Semi-Supervised Clustering via Dynamic Graph Structure Learning

no code implementations6 Sep 2022 Huaming Ling, Chenglong Bao, Xin Liang, Zuoqiang Shi

However, existing methods adopt a static affinity matrix to learn the low-dimensional representations of data points and do not optimize the affinity matrix during the learning process.

Clustering Graph structure learning

Convergence Rates of Training Deep Neural Networks via Alternating Minimization Methods

no code implementations30 Aug 2022 Jintao Xu, Chenglong Bao, Wenxun Xing

Training deep neural networks (DNNs) is an important and challenging optimization problem in machine learning due to its non-convexity and non-separable structure.

A scalable deep learning approach for solving high-dimensional dynamic optimal transport

no code implementations16 May 2022 Wei Wan, Yuejin Zhang, Chenglong Bao, Bin Dong, Zuoqiang Shi

In this work, we propose a deep learning based method to solve the dynamic optimal transport in high dimensional space.

Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach

1 code implementation21 Apr 2022 Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, Chenglong Bao

Current approaches aim at generating synthesized training data from unpaired samples by exploring the relationship between the corrupted and clean data.

Image Denoising Image Restoration +3

AFEC: Active Forgetting of Negative Transfer in Continual Learning

1 code implementation NeurIPS 2021 Liyuan Wang, Mingtian Zhang, Zhongfan Jia, Qian Li, Chenglong Bao, Kaisheng Ma, Jun Zhu, Yi Zhong

Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative.

Continual Learning Transfer Learning

Learning From Unpaired Data: A Variational Bayes Approach

no code implementations29 Sep 2021 Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, Chenglong Bao

Collecting the paired training data is a difficult task in practice, but the unpaired samples broadly exist.

Image Denoising Super-Resolution +1

A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications

no code implementations ICLR 2022 Fuchao Wei, Chenglong Bao, Yang Liu

We prove that the basic version of ST-AM is equivalent to the full-memory AM in strongly convex quadratic optimization, and with minor changes it has local linear convergence for solving general nonlinear fixed-point problems.

Image Classification Stochastic Optimization

Diffusion Mechanism in Residual Neural Network: Theory and Applications

1 code implementation7 May 2021 Tangjun Wang, Zehao Dou, Chenglong Bao, Zuoqiang Shi

In many learning tasks with limited training samples, the diffusion connects the labeled and unlabeled data points and is a critical component for achieving high classification accuracy.

Binary Classification Classification +4

Layer-wise Adversarial Defense: An ODE Perspective

no code implementations1 Jan 2021 Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi

Deep neural networks are observed to be fragile against adversarial attacks, which have dramatically limited their practical applicability.

Adversarial Defense

An Unsupervised Deep Learning Approach for Real-World Image Denoising

1 code implementation ICLR 2021 Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao

In the real-world case, the noise distribution is so complex that the simplified additive white Gaussian (AWGN) assumption rarely holds, which significantly deteriorates the Gaussian denoisers' performance.

Image Denoising

Task-Oriented Feature Distillation

1 code implementation NeurIPS 2020 Linfeng Zhang, Yukang Shi, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao

Moreover, an orthogonal loss is applied to the feature resizing layer in TOFD to improve the performance of knowledge distillation.

3D Classification General Classification +2

Interpolation between Residual and Non-Residual Networks

1 code implementation10 Jun 2020 Zonghan Yang, Yang Liu, Chenglong Bao, Zuoqiang Shi

Although ordinary differential equations (ODEs) provide insights for designing network architectures, its relationship with the non-residual convolutional neural networks (CNNs) is still unclear.

Adversarial Attack Image Classification

Exploring Frequency Domain Interpretation of Convolutional Neural Networks

no code implementations27 Nov 2019 Zhongfan Jia, Chenglong Bao, Kaisheng Ma

To the best of our knowledge, there is no study on the interpretation of modern CNNs from the perspective of the frequency proportion of filters.

Brain-inspired reverse adversarial examples

no code implementations28 May 2019 Shaokai Ye, Sia Huat Tan, Kaidi Xu, Yanzhi Wang, Chenglong Bao, Kaisheng Ma

On contrast, current state-of-the-art deep learning approaches heavily depend on the variety of training samples and the capacity of the network.

Quantization

Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

1 code implementation ICCV 2019 Linfeng Zhang, Jiebo Song, Anni Gao, Jingwei Chen, Chenglong Bao, Kaisheng Ma

Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself.

Knowledge Distillation

Wasserstein-Fisher-Rao Document Distance

no code implementations23 Apr 2019 Zihao Wang, Datong Zhou, Yong Zhang, Hao Wu, Chenglong Bao

As a fundamental problem of natural language processing, it is important to measure the distance between different documents.

Semantic Similarity Semantic Textual Similarity

Whole Brain Susceptibility Mapping Using Harmonic Incompatibility Removal

no code implementations31 May 2018 Chenglong Bao, Jae Kyu Choi, Bin Dong

Quantitative susceptibility mapping (QSM) aims to visualize the three dimensional susceptibility distribution by solving the field-to-source inverse problem using the phase data in magnetic resonance signal.

Equiangular Kernel Dictionary Learning With Applications to Dynamic Texture Analysis

no code implementations CVPR 2016 Yuhui Quan, Chenglong Bao, Hui Ji

Most existing dictionary learning algorithms consider a linear sparse model, which often cannot effectively characterize the nonlinear properties present in many types of visual data, e. g. dynamic texture (DT).

Computational Efficiency Dictionary Learning +1

l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence

no code implementations CVPR 2014 Chenglong Bao, Hui Ji, Yuhui Quan, Zuowei Shen

Sparse coding and dictionary learning have seen their applications in many vision tasks, which usually is formulated as a non-convex optimization problem.

Dictionary Learning Face Recognition

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