Search Results for author: Yang He

Found 19 papers, 8 papers with code

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

no code implementations11 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 Medical Image Segmentation

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.

DeepFake Detection Face Swapping

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

CosSGD: Nonlinear Quantization for Communication-efficient Federated Learning

no code implementations15 Dec 2020 Yang He, Maximilian Zenk, Mario Fritz

Federated learning facilitates learning across clients without transferring local data on these clients to a central server.

Federated Learning Image Classification +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 Semantic Segmentation

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 +1

Meta Filter Pruning to Accelerate Deep Convolutional Neural Networks

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

First, as a complement to the existing p-norm criterion, we introduce a new pruning criterion considering the filter relation via filter distance.

Image Classification

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 via Geometric Median for Deep Convolutional Neural Networks Acceleration

2 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

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

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.

Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes

1 code implementation ECCV 2018 Yang He, Bernt Schiele, Mario Fritz

Recent advances in Deep Learning and probabilistic modeling have led to strong improvements in generative models for images.

Conditional Image Generation

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.

Semantic Segmentation

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.

STD2P: RGBD Semantic Segmentation Using Spatio-Temporal Data-Driven Pooling

1 code implementation CVPR 2017 Yang He, Wei-Chen Chiu, Margret Keuper, Mario Fritz

The proposed network produces a high quality segmentation of a single image by leveraging information from additional views of the same scene.

Optical Flow Estimation Semantic Segmentation +1

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