Search Results for author: Liqiang Wang

Found 29 papers, 12 papers with code

CTIN: Robust Contextual Transformer Network for Inertial Navigation

1 code implementation3 Dec 2021 Bingbing Rao, Ehsan Kazemi, Yifan Ding, Devu M Shila, Frank M. Tucker, Liqiang Wang

Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements.

Multi-Task Learning

Rapid Assessments of Light-Duty Gasoline Vehicle Emissions Using On-Road Remote Sensing and Machine Learning

no code implementations1 Oct 2021 Yan Xia, Linhui Jiang, Lu Wang, Xue Chen, Jianjie Ye, Tangyan Hou, Liqiang Wang, Yibo Zhang, Mengying Li, Zhen Li, Zhe Song, Yaping Jiang, Weiping Liu, Pengfei Li, Daniel Rosenfeld, John H. Seinfeld, Shaocai Yu

Our results show that the ORRS measurements, assisted by the machine-learning-based ensemble model developed here, can realize day-to-day supervision of on-road vehicle-specific emissions.

Anti-Neuron Watermarking: Protecting Personal Data Against Unauthorized Neural Networks

1 code implementation18 Sep 2021 Zihang Zou, Boqing Gong, Liqiang Wang

We study protecting a user's data (images in this work) against a learner's unauthorized use in training neural networks.

Deep Epidemiological Modeling by Black-box Knowledge Distillation: An Accurate Deep Learning Model for COVID-19

no code implementations20 Jan 2021 Dongdong Wang, Shunpu Zhang, Liqiang Wang

Next, we use simulated observation sequences to query the simulation system to retrieve simulated projection sequences as knowledge.

Knowledge Distillation

Ranking Neural Checkpoints

1 code implementation CVPR 2021 Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, Boqing Gong

This paper is concerned with ranking many pre-trained deep neural networks (DNNs), called checkpoints, for the transfer learning to a downstream task.

Transfer Learning

Cross-Domain Learning for Classifying Propaganda in Online Contents

2 code implementations13 Nov 2020 Liqiang Wang, Xiaoyu Shen, Gerard de Melo, Gerhard Weikum

Prior work has focused on supervised learning with training data from the same domain.

Beyond the Deep Metric Learning: Enhance the Cross-Modal Matching with Adversarial Discriminative Domain Regularization

no code implementations23 Oct 2020 Li Ren, Kai Li, Liqiang Wang, Kien Hua

In this paper, we address this limitation with an efficient learning objective that considers the discriminative feature distributions between the visual objects and sentence words.

Metric Learning

Trace-Norm Adversarial Examples

no code implementations2 Jul 2020 Ehsan Kazemi, Thomas Kerdreux, Liqiang Wang

White box adversarial perturbations are sought via iterative optimization algorithms most often minimizing an adversarial loss on a $l_p$ neighborhood of the original image, the so-called distortion set.

Adversarial Robustness

Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox Model

1 code implementation CVPR 2020 Dongdong Wang, Yandong Li, Liqiang Wang, Boqing Gong

The other is that the number of images used for the knowledge distillation should be small; otherwise, it violates our expectation of reducing the dependence on large-scale datasets.

Active Learning Knowledge Distillation

BachGAN: High-Resolution Image Synthesis from Salient Object Layout

1 code implementation CVPR 2020 Yandong Li, Yu Cheng, Zhe Gan, Licheng Yu, Liqiang Wang, Jingjing Liu

We propose a new task towards more practical application for image generation - high-quality image synthesis from salient object layout.

Image Generation

Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective

1 code implementation CVPR 2020 Muhammad Abdullah Jamal, Matthew Brown, Ming-Hsuan Yang, Liqiang Wang, Boqing Gong

Object frequency in the real world often follows a power law, leading to a mismatch between datasets with long-tailed class distributions seen by a machine learning model and our expectation of the model to perform well on all classes.

Domain Adaptation Long-tail Learning +1

Self-supervised learning for audio-visual speaker diarization

no code implementations13 Feb 2020 Yifan Ding, Yong Xu, Shi-Xiong Zhang, Yahuan Cong, Liqiang Wang

Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems.

Self-Supervised Learning speaker-diarization +2

Attacking Lifelong Learning Models with Gradient Reversion

no code implementations ICLR 2020 Yunhui Guo, Mingrui Liu, Yandong Li, Liqiang Wang, Tianbao Yang, Tajana Rosing

We evaluate the effectiveness of traditional attack methods such as FGSM and PGD. The results show that A-GEM still possesses strong continual learning ability in the presence of adversarial examples in the memory and simple defense techniques such as label smoothing can further alleviate the adversarial effects.

Continual Learning

AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning

no code implementations21 Nov 2019 Yunhui Guo, Yandong Li, Liqiang Wang, Tajana Rosing

Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task.

General Classification Image Classification +1

Defending Against Adversarial Attacks Using Random Forests

no code implementations16 Jun 2019 Yifan Ding, Liqiang Wang, huan zhang, Jin-Feng Yi, Deliang Fan, Boqing Gong

As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world.

Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness

no code implementations30 May 2019 Adnan Siraj Rakin, Zhezhi He, Li Yang, Yanzhi Wang, Liqiang Wang, Deliang Fan

In this work, we show that shrinking the model size through proper weight pruning can even be helpful to improve the DNN robustness under adversarial attack.

Adversarial Attack

Frame-Recurrent Video Inpainting by Robust Optical Flow Inference

no code implementations8 May 2019 Yifan Ding, Chuan Wang, Haibin Huang, Jiaming Liu, Jue Wang, Liqiang Wang

Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.

Image Inpainting Optical Flow Estimation +1

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

1 code implementation1 May 2019 Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong

Powerful adversarial attack methods are vital for understanding how to construct robust deep neural networks (DNNs) and for thoroughly testing defense techniques.

Adversarial Attack

Learning to Adaptively Scale Recurrent Neural Networks

no code implementations15 Feb 2019 Hao Hu, Liqiang Wang, Guo-Jun Qi

Recent advancements in recurrent neural network (RNN) research have demonstrated the superiority of utilizing multiscale structures in learning temporal representations of time series.

Time Series

Asynchronous Delay-Aware Accelerated Proximal Coordinate Descent for Nonconvex Nonsmooth Problems

no code implementations5 Feb 2019 Ehsan Kazemi, Liqiang Wang

To the best of our knowledge, we are the first to provide stochastic and deterministic accelerated extension of APCD algorithms for general nonconvex and nonsmooth problems ensuring that for both bounded delays and unbounded delays every limit point is a critical point.

Depthwise Convolution is All You Need for Learning Multiple Visual Domains

1 code implementation3 Feb 2019 Yunhui Guo, Yandong Li, Rogerio Feris, Liqiang Wang, Tajana Rosing

A model aware of the relationships between different domains can also be trained to work on new domains with less resources.

Continual Learning

AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data

1 code implementation CVPR 2019 Liheng Zhang, Guo-Jun Qi, Liqiang Wang, Jiebo Luo

The success of deep neural networks often relies on a large amount of labeled examples, which can be difficult to obtain in many real scenarios.

Representation Learning

Multi-Stream Dynamic Video Summarization

1 code implementation1 Dec 2018 Mohamed Elfeki, Liqiang Wang, Ali Borji

With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content.

Video Summarization

Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect

1 code implementation ICLR 2018 Xiang Wei, Boqing Gong, Zixia Liu, Wei Lu, Liqiang Wang

Despite being impactful on a variety of problems and applications, the generative adversarial nets (GANs) are remarkably difficult to train.

A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

no code implementations8 Feb 2018 Yifan Ding, Liqiang Wang, Deliang Fan, Boqing Gong

In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability.

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