Search Results for author: Yang He

Found 29 papers, 12 papers with code

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration

3 code implementations CVPR 2019 Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, Yi Yang

In this paper, we analyze this norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1) the norm deviation of the filters should be large; (2) the minimum norm of the filters should be small.

Image Classification

Structured Pruning for Deep Convolutional Neural Networks: A survey

1 code implementation1 Mar 2023 Yang He, Lingao Xiao

The remarkable performance of deep Convolutional neural networks (CNNs) is generally attributed to their deeper and wider architectures, which can come with significant computational costs.

Network Pruning Neural Architecture Search

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

6 code implementations21 Aug 2018 Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, Yi Yang

Therefore, the network trained by our method has a larger model capacity to learn from the training data.

Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis

1 code implementation29 May 2021 Yang He, Ning Yu, Margret Keuper, Mario Fritz

The rapid advances in deep generative models over the past years have led to highly {realistic media, known as deepfakes,} that are commonly indistinguishable from real to human eyes.

Colorization Denoising +2

ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

1 code implementation11 Oct 2021 Hui-Po Wang, Sebastian U. Stich, Yang He, Mario Fritz

Federated learning is a powerful distributed learning scheme that allows numerous edge devices to collaboratively train a model without sharing their data.

Federated Learning Image Segmentation +2

Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks

2 code implementations22 Aug 2018 Yang He, Xuanyi Dong, Guoliang Kang, Yanwei Fu, Chenggang Yan, Yi Yang

With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable.

Image Classification

You Only Condense Once: Two Rules for Pruning Condensed Datasets

1 code implementation NeurIPS 2023 Yang He, Lingao Xiao, Joey Tianyi Zhou

However, these scenarios have two significant challenges: 1) the varying computational resources available on the devices require a dataset size different from the pre-defined condensed dataset, and 2) the limited computational resources often preclude the possibility of conducting additional condensation processes.

Dataset Condensation

Learning Dilation Factors for Semantic Segmentation of Street Scenes

1 code implementation6 Sep 2017 Yang He, Margret Keuper, Bernt Schiele, Mario Fritz

In this paper, we present an approach for learning dilation parameters adaptively per channel, consistently improving semantic segmentation results on street-scene datasets like Cityscapes and Camvid.

Segmentation Semantic Segmentation

Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain

1 code implementation9 Oct 2023 Junru Zhang, Lang Feng, Yang He, Yuhan Wu, Yabo Dong

While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms.

Time Series Time Series Classification

Telepath: Understanding Users from a Human Vision Perspective in Large-Scale Recommender Systems

no code implementations1 Sep 2017 Yu Wang, Jixing Xu, Aohan Wu, Mantian Li, Yang He, Jinghe Hu, Weipeng P. Yan

This paper proposes Telepath, a vision-based bionic recommender system model, which understands users from such perspective.

Recommendation Systems

LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions

no code implementations18 Aug 2017 Yu Wang, Jiayi Liu, Yuxiang Liu, Jun Hao, Yang He, Jinghe Hu, Weipeng P. Yan, Mantian Li

We present LADDER, the first deep reinforcement learning agent that can successfully learn control policies for large-scale real-world problems directly from raw inputs composed of high-level semantic information.

Stochastic Model Pruning via Weight Dropping Away and Back

no code implementations5 Dec 2018 Haipeng Jia, Xueshuang Xiang, Da Fan, Meiyu Huang, Changhao Sun, Yang He

Addressing these two issues, this paper proposes the Drop Pruning approach, which leverages stochastic optimization in the pruning process by introducing a drop strategy at each pruning step, namely, drop away, which stochastically deletes some unimportant weights, and drop back, which stochastically recovers some pruned weights.

Model Compression Stochastic Optimization

Filter Pruning by Switching to Neighboring CNNs with Good Attributes

no code implementations8 Apr 2019 Yang He, Ping Liu, Linchao Zhu, Yi Yang

In addition, when evaluating the filter importance, only the magnitude information of the filters is considered.

Attribute Image Classification

Progressive Local Filter Pruning for Image Retrieval Acceleration

no code implementations24 Jan 2020 Xiaodong Wang, Zhedong Zheng, Yang He, Fei Yan, Zhiqiang Zeng, Yi Yang

To verify this, we evaluate our method on two widely-used image retrieval datasets, i. e., Oxford5k and Paris6K, and one person re-identification dataset, i. e., Market-1501.

Image Retrieval Network Pruning +2

Synthetic Convolutional Features for Improved Semantic Segmentation

no code implementations18 Sep 2020 Yang He, Bernt Schiele, Mario Fritz

Recently, learning-based image synthesis has enabled to generate high-resolution images, either applying popular adversarial training or a powerful perceptual loss.

Image Generation Segmentation +1

CosSGD: Communication-Efficient Federated Learning with a Simple Cosine-Based Quantization

no code implementations15 Dec 2020 Yang He, Hui-Po Wang, Maximilian Zenk, Mario Fritz

Despite notable progress in gradient compression, the existing quantization methods require further improvement when low-bits compression is applied, especially the overall systems often degenerate a lot when quantization are applied in double directions to compress model weights and gradients.

Federated Learning Image Classification +2

Automated Deepfake Detection

no code implementations20 Jun 2021 Ping Liu, Yuewei Lin, Yang He, Yunchao Wei, Liangli Zhen, Joey Tianyi Zhou, Rick Siow Mong Goh, Jingen Liu

In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.

BIG-bench Machine Learning DeepFake Detection +1

A Parameter-free Nonconvex Low-rank Tensor Completion Model for Spatiotemporal Traffic Data Recovery

no code implementations28 Sep 2022 Yang He, Yuheng Jia, Liyang Hu, Chengchuan An, Zhenbo Lu, Jingxin Xia

In this study, we proposed a Parameter-Free Non-Convex Tensor Completion model (TC-PFNC) for traffic data recovery, in which a log-based relaxation term was designed to approximate tensor algebraic rank.

Urban Scene Semantic Segmentation with Low-Cost Coarse Annotation

no code implementations15 Dec 2022 Anurag Das, Yongqin Xian, Yang He, Zeynep Akata, Bernt Schiele

For best performance, today's semantic segmentation methods use large and carefully labeled datasets, requiring expensive annotation budgets.

Data Augmentation Scene Segmentation +1

Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing

no code implementations6 Sep 2023 Sichao Fu, Qinmu Peng, Yang He, Baokun Du, Xinge You

In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks.

Node Classification Self-Supervised Learning

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

no code implementations30 Dec 2023 Yao Wan, Yang He, Zhangqian Bi, JianGuo Zhang, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin, Philip S. Yu

We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.

Representation Learning

Multisize Dataset Condensation

no code implementations10 Mar 2024 Yang He, Lingao Xiao, Joey Tianyi Zhou, Ivor Tsang

These two challenges connect to the "subset degradation problem" in traditional dataset condensation: a subset from a larger condensed dataset is often unrepresentative compared to directly condensing the whole dataset to that smaller size.

Dataset Condensation

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