Search Results for author: Xin Lou

Found 6 papers, 2 papers with code

Raw Bayer Pattern Image Synthesis for Computer Vision-oriented Image Signal Processing Pipeline Design

no code implementations25 Oct 2021 Wei Zhou, Xiangyu Zhang, Hongyu Wang, Shenghua Gao, Xin Lou

It is shown that by adding another transformation, the proposed method is able to synthesize high-quality RAW Bayer images with arbitrary size.

Demosaicking Image Generation +3

On Lightweight Privacy-Preserving Collaborative Learning for Internet of Things by Independent Random Projections

1 code implementation11 Dec 2020 Linshan Jiang, Rui Tan, Xin Lou, Guosheng Lin

This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator.

BIG-bench Machine Learning Privacy Preserving

Gradient-based Feature Extraction From Raw Bayer Pattern Images

no code implementations6 Apr 2020 Wei Zhou, Ling Zhang, Shengyu Gao, Xin Lou

In this paper, the impact of demosaicing on gradient extraction is studied and a gradient-based feature extraction pipeline based on raw Bayer pattern images is proposed.

Demosaicking Pedestrian Detection

Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

no code implementations27 Feb 2020 Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett

Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks.

Model Compression

On Lightweight Privacy-Preserving Collaborative Learning for IoT Objects

no code implementations13 Feb 2019 Linshan Jiang, Rui Tan, Xin Lou, Guosheng Lin

This paper considers the design and implementation of a practical privacy-preserving collaborative learning scheme, in which a curious learning coordinator trains a better machine learning model based on the data samples contributed by a number of IoT objects, while the confidentiality of the raw forms of the training data is protected against the coordinator.

BIG-bench Machine Learning Privacy Preserving

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