HUAWEI Technologies Co., Ltd.

research

∙ 02/23/2020

Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks

Deep neural networks often consist of a great number of trainable parame...

Yehui Tang, et al. ∙

research

∙ 03/15/2021

Learning Frequency-aware Dynamic Network for Efficient Super-Resolution

Deep learning based methods, especially convolutional neural networks (C...

Wenbin Xie, et al. ∙

research

∙ 08/04/2020

Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap

Neural architecture search (NAS) has attracted increasing attentions in ...

Lingxi Xie, et al. ∙

research

∙ 01/25/2021

AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence

Convolutional neural networks (CNN) have been widely used for boosting t...

Yunhe Wang, et al. ∙

research

∙ 05/17/2021

Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report

Video super-resolution has recently become one of the most important mob...

Andrey Ignatov, et al. ∙

research

∙ 12/10/2019

Appending Adversarial Frames for Universal Video Attack

There have been many efforts in attacking image classification models wi...

Zhikai Chen, et al. ∙

research

∙ 12/22/2021

Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description Extraction

Learnable keypoint detectors and descriptors are beginning to outperform...

Henrique Siqueira, et al. ∙

research

∙ 12/31/2019

AdderNet: Do We Really Need Multiplications in Deep Learning?

Compared with cheap addition operation, multiplication operation is of m...

Hanting Chen, et al. ∙

research

∙ 09/25/2019

Revisit Knowledge Distillation: a Teacher-free Framework

Knowledge Distillation (KD) aims to distill the knowledge of a cumbersom...

Li Yuan, et al. ∙

research

∙ 07/07/2020

Towards a practical measure of interference for reinforcement learning

Catastrophic interference is common in many network-based learning syste...

Vincent Liu, et al. ∙

research

∙ 05/08/2020

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

This paper reviews the NTIRE 2020 challenge on real image denoising with...

Abdelrahman Abdelhamed, et al. ∙

research

∙ 08/27/2021

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

Autonomous driving has attracted much attention over the years but turns...

Kai Chen, et al. ∙

research

∙ 03/22/2019

Few-shot Adaptive Faster R-CNN

To mitigate the detection performance drop caused by domain shift, we ai...

PetsTime, et al. ∙

research

∙ 10/03/2019

Is Fast Adaptation All You Need?

Gradient-based meta-learning has proven to be highly effective at learni...

Khurram Javed, et al. ∙

research

∙ 11/21/2019

RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving

In this paper, we strive for solving the ambiguities arisen by the astou...

Jean Marie Uwabeza Vianney, et al. ∙

research

∙ 11/27/2019

GhostNet: More Features from Cheap Operations

Deploying convolutional neural networks (CNNs) on embedded devices is di...

Kai Han, et al. ∙

research

∙ 03/15/2022

CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving

Contemporary deep-learning object detection methods for autonomous drivi...

Kaican Li, et al. ∙

research

∙ 08/23/2019

AdvHat: Real-world adversarial attack on ArcFace Face ID system

In this paper we propose a novel easily reproducible technique to attack...

Stepan Komkov, et al. ∙

research

∙ 12/03/2019

Less Is Better: Unweighted Data Subsampling via Influence Function

In the time of Big Data, training complex models on large-scale data set...

Zifeng Wang, et al. ∙

research

∙ 04/14/2020

Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

The impressive performance of deep convolutional neural networks in sing...

Mateusz Michalkiewicz, et al. ∙

research

∙ 06/21/2021

SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving

Aiming at facilitating a real-world, ever-evolving and scalable autonomo...

Jianhua Han, et al. ∙

research

∙ 04/25/2022

ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching

Graph Neural Networks (GNNs) with attention have been successfully appli...

Yan Shi, et al. ∙

research

∙ 01/21/2020

Generate High-Resolution Adversarial Samples by Identifying Effective Features

As the prevalence of deep learning in computer vision, adversarial sampl...

Sizhe Chen, et al. ∙

research

∙ 08/17/2021

Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

Instance segmentation in 3D scenes is fundamental in many applications o...

Zhihao Liang, et al. ∙

research

∙ 09/11/2019

CARS: Continuous Evolution for Efficient Neural Architecture Search

Searching techniques in most of existing neural architecture search (NAS...

Zhaohui Yang, et al. ∙

research

∙ 04/25/2020

Deep Multimodal Neural Architecture Search

Designing effective neural networks is fundamentally important in deep m...

Zhou Yu, et al. ∙

research

∙ 12/25/2021

NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification

Deep neural networks (DNNs) have demonstrated their outperformance in va...

Haibin Zheng, et al. ∙

research

∙ 10/14/2019

Real-world attack on MTCNN face detection system

Recent studies proved that deep learning approaches achieve remarkable r...

Edgar Kaziakhmedov, et al. ∙

research

∙ 12/31/2019

Scalable NAS with Factorizable Architectural Parameters

Neural architecture search (NAS) is an emerging topic in machine learnin...

Lanfei Wang, et al. ∙

research

∙ 09/10/2019

Pose Agnostic Cross-spectral Hallucination via Disentangling Independent Factors

The cross-sensor gap is one of the challenges that arise much research i...

Boyan Duan, et al. ∙

research

∙ 06/07/2020

Optimally Combining Classifiers for Semi-Supervised Learning

This paper considers semi-supervised learning for tabular data. It is wi...

Zhiguo Wang, et al. ∙

research

∙ 10/02/2018

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Over the last years, the computational power of mobile devices such as s...

Andrey Ignatov, et al. ∙

research

∙ 09/29/2019

SteReFo: Efficient Image Refocusing with Stereo Vision

Whether to attract viewer attention to a particular object, give the imp...

Benjamin Busam, et al. ∙

research

∙ 10/10/2019

Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling

In real world machine learning applications, testing data may contain so...

Changying Du, et al. ∙

research

∙ 05/13/2020

Coordinates-based Resource Allocation Through Supervised Machine Learning

Appropriate allocation of system resources is essential for meeting the ...

Sahar Imtiaz, et al. ∙

research

∙ 01/17/2022

An Improved Reinforcement Learning Algorithm for Learning to Branch

Most combinatorial optimization problems can be formulated as mixed inte...

Qingyu Qu, et al. ∙

research

∙ 03/14/2022

From Big to Small: Adaptive Learning to Partial-Set Domains

Domain adaptation targets at knowledge acquisition and dissemination fro...

Zhangjie Cao, et al. ∙

research

∙ 03/07/2020

Distilling portable Generative Adversarial Networks for Image Translation

Despite Generative Adversarial Networks (GANs) have been widely used in ...

Hanting Chen, et al. ∙

research

∙ 05/26/2020

Explore Training of Deep Convolutional Neural Networks on Battery-powered Mobile Devices: Design and Application

The fast-growing smart applications on mobile devices leverage pre-train...

Cong Wang, et al. ∙

research

∙ 07/12/2021

Zero-shot Visual Question Answering using Knowledge Graph

Incorporating external knowledge to Visual Question Answering (VQA) has ...

Zhuo Chen, et al. ∙

research

∙ 07/20/2021

Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters

In this paper, we focus on a less explored, but more realistic and compl...

Mrigank Rochan, et al. ∙

research

∙ 09/01/2021

Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep

In indoor positioning, signal fluctuation is highly location-dependent. ...

Ran Guan, et al. ∙

research

∙ 12/25/2018

PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning

Deep neural networks have demonstrated cutting edge performance on vario...

Mehdi Jafarnia-Jahromi, et al. ∙

research

∙ 02/17/2020

Discernible Compressed Images via Deep Perception Consistency

Image compression, as one of the fundamental low-level image processing ...

Zhaohui Yang, et al. ∙

research

∙ 05/29/2020

HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens

Neural Architecture Search (NAS) refers to automatically design the arch...

Zhaohui Yang, et al. ∙

research

∙ 06/24/2020

DeepMnemonic: Password Mnemonic Generation via Deep Attentive Encoder-Decoder Model

Strong passwords are fundamental to the security of password-based user ...

Yao Cheng, et al. ∙

research

∙ 09/01/2020

Bidirectional Attention Network for Monocular Depth Estimation

In this paper, we propose a Bidirectional Attention Network (BANet), an ...

Shubhra Aich, et al. ∙

research

∙ 06/22/2021

LV-BERT: Exploiting Layer Variety for BERT

Modern pre-trained language models are mostly built upon backbones stack...

Weihao Yu, et al. ∙

research

∙ 08/19/2021

Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain

Recently, the generalization behavior of Convolutional Neural Networks (...

Guangyao Chen, et al. ∙

research

∙ 08/26/2021

Human readable network troubleshooting based on anomaly detection and feature scoring

Network troubleshooting is still a heavily human-intensive process. To r...

Jose M. Navarro, et al. ∙

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