Search Results for author: Yiwen Guo

Found 24 papers, 10 papers with code

Robust Coreset for Continuous-and-Bounded Learning (with Outliers)

no code implementations30 Jun 2021 Zixiu Wang, Yiwen Guo, Hu Ding

In this paper, we propose a novel robust coreset method for the {\em continuous-and-bounded learning} problem (with outliers) which includes a broad range of popular optimization objectives in machine learning, like logistic regression and $ k $-means clustering.

Recent Advances in Large Margin Learning

no code implementations25 Mar 2021 Yiwen Guo, ChangShui Zhang

This paper serves as a survey of recent advances in large margin training and its theoretical foundations, mostly for (nonlinear) deep neural networks (DNNs) that are probably the most prominent machine learning models for large-scale data in the community over the past decade.

Deepfake Forensics via An Adversarial Game

no code implementations25 Mar 2021 Zhi Wang, Yiwen Guo, WangMeng Zuo

In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities.

Classification DeepFake Detection +2

Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples

no code implementations ICLR 2021 Ziang Yan, Yiwen Guo, Jian Liang, ChangShui Zhang

To craft black-box adversarial examples, adversaries need to query the victim model and take proper advantage of its feedback.

Image Classification

Learned ISTA with Error-based Thresholding for Adaptive Sparse Coding

no code implementations1 Jan 2021 Li Ziang, Wu Kailun, Yiwen Guo, ChangShui Zhang

The learned iterative shrinkage thresholding algorithm (LISTA) introduces deep unfolding models with learnable thresholds in the shrinkage function for sparse coding.

Backpropagating Linearly Improves Transferability of Adversarial Examples

1 code implementation NeurIPS 2020 Yiwen Guo, Qizhang Li, Hao Chen

The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great attention from the community.

Practical No-box Adversarial Attacks against DNNs

1 code implementation NeurIPS 2020 Qizhang Li, Yiwen Guo, Hao Chen

We propose three mechanisms for training with a very small dataset (on the order of tens of examples) and find that prototypical reconstruction is the most effective.

Face Verification Image Classification

Yet Another Intermediate-Level Attack

1 code implementation ECCV 2020 Qizhang Li, Yiwen Guo, Hao Chen

The transferability of adversarial examples across deep neural network (DNN) models is the crux of a spectrum of black-box attacks.

On Connections between Regularizations for Improving DNN Robustness

no code implementations4 Jul 2020 Yiwen Guo, Long Chen, Yurong Chen, Chang-Shui Zhang

This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs), from a theoretical point of view.

Image Classification

Towards Certified Robustness of Metric Learning

no code implementations10 Jun 2020 Xiaochen Yang, Yiwen Guo, Mingzhi Dong, Jing-Hao Xue

Metric learning aims to learn a distance metric such that semantically similar instances are pulled together while dissimilar instances are pushed away.

Metric Learning

Sparse Coding with Gated Learned ISTA

1 code implementation ICLR 2020 Kailun Wu, Yiwen Guo, Ziang Li, Chang-Shui Zhang

In this paper, we study the learned iterative shrinkage thresholding algorithm (LISTA) for solving sparse coding problems.

Adversarial Margin Maximization Networks

1 code implementation14 Nov 2019 Ziang Yan, Yiwen Guo, Chang-Shui Zhang

The tremendous recent success of deep neural networks (DNNs) has sparked a surge of interest in understanding their predictive ability.

Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks

2 code implementations NeurIPS 2019 Ziang Yan, Yiwen Guo, Chang-Shui Zhang

Unlike the white-box counterparts that are widely studied and readily accessible, adversarial examples in black-box settings are generally more Herculean on account of the difficulty of estimating gradients.

Adversarial Attack

Differentiable Architecture Search with Ensemble Gumbel-Softmax

no code implementations6 May 2019 Jianlong Chang, Xinbang Zhang, Yiwen Guo, Gaofeng Meng, Shiming Xiang, Chunhong Pan

For network architecture search (NAS), it is crucial but challenging to simultaneously guarantee both effectiveness and efficiency.

Neural Architecture Search

Deep Discriminative Clustering Analysis

no code implementations5 May 2019 Jianlong Chang, Yiwen Guo, Lingfeng Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

Traditional clustering methods often perform clustering with low-level indiscriminative representations and ignore relationships between patterns, resulting in slight achievements in the era of deep learning.

Sparse DNNs with Improved Adversarial Robustness

no code implementations NeurIPS 2018 Yiwen Guo, Chao Zhang, Chang-Shui Zhang, Yurong Chen

Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications.

General Classification

Deep Defense: Training DNNs with Improved Adversarial Robustness

1 code implementation NeurIPS 2018 Ziang Yan, Yiwen Guo, Chang-Shui Zhang

Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems.

Network Sketching: Exploiting Binary Structure in Deep CNNs

no code implementations CVPR 2017 Yiwen Guo, Anbang Yao, Hao Zhao, Yurong Chen

Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks.

Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

3 code implementations10 Feb 2017 Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen

The weights in the other group are responsible to compensate for the accuracy loss from the quantization, thus they are the ones to be re-trained.


Dynamic Network Surgery for Efficient DNNs

4 code implementations NeurIPS 2016 Yiwen Guo, Anbang Yao, Yurong Chen

In this paper, we propose a novel network compression method called dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning.

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