Search Results for author: Mingqing Xiao

Found 10 papers, 8 papers with code

Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks

1 code implementation19 Feb 2024 Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin

Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications.

Continual Learning

Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networks

1 code implementation ICCV 2023 Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo

In particular, our method achieves state-of-the-art accuracy on ImageNet, while the memory cost and training time are reduced by more than 70% and 50%, respectively, compared with BPTT.

SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks

1 code implementation1 Feb 2023 Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin

In this paper, we study spike-based implicit differentiation on the equilibrium state (SPIDE) that extends the recently proposed training method, implicit differentiation on the equilibrium state (IDE), for supervised learning with purely spike-based computation, which demonstrates the potential for energy-efficient training of SNNs.

Online Training Through Time for Spiking Neural Networks

1 code implementation9 Oct 2022 Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin

With OTTT, it is the first time that two mainstream supervised SNN training methods, BPTT with SG and spike representation-based training, are connected, and meanwhile in a biologically plausible form.

Event data classification Gesture Recognition +1

Invertible Rescaling Network and Its Extensions

1 code implementation9 Oct 2022 Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu

To be specific, we develop invertible models to generate valid degraded images and meanwhile transform the distribution of lost contents to the fixed distribution of a latent variable during the forward degradation.

Colorization Image Compression

Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation

1 code implementation CVPR 2022 Qingyan Meng, Mingqing Xiao, Shen Yan, Yisen Wang, Zhouchen Lin, Zhi-Quan Luo

In this paper, we propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance that is competitive to ANNs yet with low latency.

Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State

1 code implementation NeurIPS 2021 Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Yisen Wang, Zhouchen Lin

In this work, we consider feedback spiking neural networks, which are more brain-like, and propose a novel training method that does not rely on the exact reverse of the forward computation.

Modeling Lost Information in Lossy Image Compression

no code implementations22 Jun 2020 Yaolong Wang, Mingqing Xiao, Chang Liu, Shuxin Zheng, Tie-Yan Liu

Specifically, ILC introduces an invertible encoding module to replace the encoder-decoder structure to produce the low dimensional informative latent representation, meanwhile, transform the lost information into an auxiliary latent variable that won't be further coded or stored.

Image Compression

Invertible Image Rescaling

10 code implementations ECCV 2020 Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu

High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images.

Image Super-Resolution

TDAPNet: Prototype Network with Recurrent Top-Down Attention for Robust Object Classification under Partial Occlusion

no code implementations9 Sep 2019 Mingqing Xiao, Adam Kortylewski, Ruihai Wu, Siyuan Qiao, Wei Shen, Alan Yuille

Despite deep convolutional neural networks' great success in object classification, it suffers from severe generalization performance drop under occlusion due to the inconsistency between training and testing data.

General Classification Object +1

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