Search Results for author: Guoqi Li

Found 60 papers, 24 papers with code

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

1 code implementation25 Oct 2023 Wei Fang, Yanqi Chen, Jianhao Ding, Zhaofei Yu, Timothée Masquelier, Ding Chen, Liwei Huang, Huihui Zhou, Guoqi Li, Yonghong Tian

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties.

Code Generation

Inherent Redundancy in Spiking Neural Networks

1 code implementation ICCV 2023 Man Yao, Jiakui Hu, Guangshe Zhao, Yaoyuan Wang, Ziyang Zhang, Bo Xu, Guoqi Li

In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs.

Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks

1 code implementation12 Aug 2023 Xuerui Qiu, Rui-Jie Zhu, Yuhong Chou, Zhaorui Wang, Liang-Jian Deng, Guoqi Li

Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency.

 Ranked #1 on Image Classification on ImageNet (energy consumption metric)

Efficient Neural Network Image Classification

Deep Directly-Trained Spiking Neural Networks for Object Detection

1 code implementation ICCV 2023 Qiaoyi Su, Yuhong Chou, Yifan Hu, Jianing Li, Shijie Mei, Ziyang Zhang, Guoqi Li

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode information in spatiotemporal dynamics.

Object object-detection +1

Spike-driven Transformer

1 code implementation NeurIPS 2023 Man Yao, Jiakui Hu, Zhaokun Zhou, Li Yuan, Yonghong Tian, Bo Xu, Guoqi Li

In this paper, we incorporate the spike-driven paradigm into Transformer by the proposed Spike-driven Transformer with four unique properties: 1) Event-driven, no calculation is triggered when the input of Transformer is zero; 2) Binary spike communication, all matrix multiplications associated with the spike matrix can be transformed into sparse additions; 3) Self-attention with linear complexity at both token and channel dimensions; 4) The operations between spike-form Query, Key, and Value are mask and addition.

Theoretical foundations of studying criticality in the brain

no code implementations9 Jun 2023 Yang Tian, Zeren Tan, Hedong Hou, Guoqi Li, Aohua Cheng, Yike Qiu, Kangyu Weng, Chun Chen, Pei Sun

These problems stem from the non-triviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data.

Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis

no code implementations8 Jun 2023 Gongshu Wang, Ning Jiang, Yunxiao Ma, Tiantian Liu, Duanduan Chen, Jinglong Wu, Guoqi Li, Dong Liang, Tianyi Yan

In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis.

Representation Learning

Sufficient Control of Complex Networks

no code implementations10 Mar 2023 Xiang Li, Guoqi Li, Leitao Gao, Beibei Li, Gaoxi Xiao

In this paper, we propose to study on sufficient control of complex networks which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters.

SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

1 code implementation27 Feb 2023 Rui-Jie Zhu, Qihang Zhao, Guoqi Li, Jason K. Eshraghian

As a result, their performance lags behind modern deep learning, and we are yet to see the effectiveness of SNNs in language generation.

Language Modelling Text Generation

Training Full Spike Neural Networks via Auxiliary Accumulation Pathway

2 code implementations27 Jan 2023 Guangyao Chen, Peixi Peng, Guoqi Li, Yonghong Tian

The accumulation in AAP could compensate for the information loss during the forward and backward of full spike propagation, and facilitate the training of the FSNN.

MSS-DepthNet: Depth Prediction with Multi-Step Spiking Neural Network

no code implementations22 Nov 2022 Xiaoshan Wu, Weihua He, Man Yao, Ziyang Zhang, Yaoyuan Wang, Guoqi Li

Spiking neural network is a novel event-based computational paradigm that is considered to be well suited for processing event camera tasks.

Computational Efficiency Depth Estimation +1

HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors

2 code implementations17 Nov 2022 Xiao Wang, Zongzhen Wu, Bo Jiang, Zhimin Bao, Lin Zhu, Guoqi Li, YaoWei Wang, Yonghong Tian

The main streams of human activity recognition (HAR) algorithms are developed based on RGB cameras which are suffered from illumination, fast motion, privacy-preserving, and large energy consumption.

Activity Prediction Human Activity Recognition +1

Attention Spiking Neural Networks

no code implementations28 Sep 2022 Man Yao, Guangshe Zhao, Hengyu Zhang, Yifan Hu, Lei Deng, Yonghong Tian, Bo Xu, Guoqi Li

On ImageNet-1K, we achieve top-1 accuracy of 75. 92% and 77. 08% on single/4-step Res-SNN-104, which are state-of-the-art results in SNNs.

Action Recognition Image Classification

Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations

1 code implementation8 Jul 2022 Yu Du, Miaojing Shi, Fangyun Wei, Guoqi Li

In this paper, we propose a new framework to boost ZSL by explicitly learning attribute prototypes beyond images and contrastively optimizing them with attribute-level features within images.

Attribute Zero-Shot Learning

Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model

1 code implementation CVPR 2022 Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, Guoqi Li

In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model.

Image Classification Language Modelling +5

Rethinking Pretraining as a Bridge from ANNs to SNNs

1 code implementation2 Mar 2022 Yihan Lin, Yifan Hu, Shijie Ma, Guoqi Li, Dongjie Yu

In this work, a new SNN training paradigm is proposed by combining the concepts of the two different training methods with the help of the pretrain technique and BP-based deep SNN training mechanism.

Survey on Graph Neural Network Acceleration: An Algorithmic Perspective

no code implementations10 Feb 2022 Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie

Next, we provide comparisons from aspects of the efficiency and characteristics of these methods.

Advancing Spiking Neural Networks towards Deep Residual Learning

1 code implementation15 Dec 2021 Yifan Hu, Lei Deng, Yujie Wu, Man Yao, Guoqi Li

Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice.

Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks

no code implementations9 Dec 2021 Yifan Hu, Yujie Wu, Lei Deng, Guoqi Li

In this paper, we identify the crux and then propose a novel residual block for SNNs, which is able to significantly extend the depth of directly trained SNNs, e. g., up to 482 layers on CIFAR-10 and 104 layers on ImageNet, without observing any slight degradation problem.

ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks

1 code implementation23 Oct 2021 Yihan Lin, Wei Ding, Shaohua Qiang, Lei Deng, Guoqi Li

With event-driven algorithms, especially the spiking neural networks (SNNs), achieving continuous improvement in neuromorphic vision processing, a more challenging event-stream-dataset is urgently needed.

Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions

no code implementations30 Aug 2021 Yang Wu, Dingheng Wang, Xiaotong Lu, Fan Yang, Guoqi Li, Weisheng Dong, Jianbo Shi

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence.

H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks

no code implementations25 Jul 2021 Ling Liang, Zheng Qu, Zhaodong Chen, Fengbin Tu, Yujie Wu, Lei Deng, Guoqi Li, Peng Li, Yuan Xie

Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits their application in many practical tasks.

Vocal Bursts Intensity Prediction

Temporal-wise Attention Spiking Neural Networks for Event Streams Classification

no code implementations ICCV 2021 Man Yao, Huanhuan Gao, Guangshe Zhao, Dingheng Wang, Yihan Lin, ZhaoXu Yang, Guoqi Li

However, when aggregating individual events into frames with a new higher temporal resolution, existing SNN models do not attach importance to that the serial frames have different signal-to-noise ratios since event streams are sparse and non-uniform.

Audio Classification Gesture Recognition +1

Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning

no code implementations30 Jun 2021 Mingkun Xu, Yujie Wu, Lei Deng, Faqiang Liu, Guoqi Li, Jing Pei

Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex environments.

Graph Attention Graph Learning +1

Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization

no code implementations27 May 2021 Yukuan Yang, Xiaowei Chi, Lei Deng, Tianyi Yan, Feng Gao, Guoqi Li

In summary, the EOQ framework is specially designed for reducing the high cost of convolution and BN in network training, demonstrating a broad application prospect of online training in resource-limited devices.

Model Compression Quantization

Sampling methods for efficient training of graph convolutional networks: A survey

no code implementations10 Mar 2021 Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan

Graph Convolutional Networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations.

Redefining Self-Normalization Property

no code implementations1 Jan 2021 Zhaodong Chen, Zhao WeiQin, Lei Deng, Guoqi Li, Yuan Xie

Moreover, analysis on the activation's mean in the forward pass reveals that the self-normalization property gets weaker with larger fan-in of each layer, which explains the performance degradation on large benchmarks like ImageNet.

Data Augmentation

Training and Inference for Integer-Based Semantic Segmentation Network

no code implementations30 Nov 2020 Jiayi Yang, Lei Deng, Yukuan Yang, Yuan Xie, Guoqi Li

However, neural network quantization can be used to reduce computation load while maintaining comparable accuracy and original network structure.

Quantization Segmentation +1

LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing

no code implementations12 Nov 2020 Zhenzhi Wu, Hehui Zhang, Yihan Lin, Guoqi Li, Meng Wang, Ye Tang

To address this issue, in this work, we propose a Leaky Integrate and Analog Fire (LIAF) neuron model, so that analog values can be transmitted among neurons, and a deep network termed as LIAF-Net is built on it for efficient spatiotemporal processing.

Question Answering

Going Deeper With Directly-Trained Larger Spiking Neural Networks

2 code implementations29 Oct 2020 Hanle Zheng, Yujie Wu, Lei Deng, Yifan Hu, Guoqi Li

To this end, we propose a threshold-dependent batch normalization (tdBN) method based on the emerging spatio-temporal backpropagation, termed "STBP-tdBN", enabling direct training of a very deep SNN and the efficient implementation of its inference on neuromorphic hardware.

Restoring Negative Information in Few-Shot Object Detection

1 code implementation NeurIPS 2020 Yukuan Yang, Fangyun Wei, Miaojing Shi, Guoqi Li

In this paper, we restore the negative information in few-shot object detection by introducing a new negative- and positive-representative based metric learning framework and a new inference scheme with negative and positive representatives.

Few-Shot Learning Few-Shot Object Detection +4

Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs

no code implementations21 Aug 2020 Dingheng Wang, Bijiao Wu, Guangshe Zhao, Man Yao, Hengnu Chen, Lei Deng, Tianyi Yan, Guoqi Li

Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data, such as natural language processing and video recognition.

Tensor Decomposition Video Recognition

Hybrid Tensor Decomposition in Neural Network Compression

no code implementations29 Jun 2020 Bijiao Wu, Dingheng Wang, Guangshe Zhao, Lei Deng, Guoqi Li

We further theoretically and experimentally discover that the HT format has better performance on compressing weight matrices, while the TT format is more suited for compressing convolutional kernels.

Neural Network Compression Tensor Decomposition

Brain-inspired global-local learning incorporated with neuromorphic computing

no code implementations5 Jun 2020 Yujie Wu, Rong Zhao, Jun Zhu, Feng Chen, Mingkun Xu, Guoqi Li, Sen Song, Lei Deng, Guanrui Wang, Hao Zheng, Jing Pei, Youhui Zhang, Mingguo Zhao, Luping Shi

We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.

Continual Learning Few-Shot Learning

Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences

1 code implementation2 May 2020 Weihua He, Yujie Wu, Lei Deng, Guoqi Li, Haoyu Wang, Yang Tian, Wei Ding, Wenhui Wang, Yuan Xie

Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion.

Fairness Gesture Recognition

Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

no code implementations1 Jan 2020 Ling Liang, Xing Hu, Lei Deng, Yujie Wu, Guoqi Li, Yufei Ding, Peng Li, Yuan Xie

Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given Spatio-temporal gradient maps.

Adversarial Attack

A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks

1 code implementation1 Jan 2020 Zhaodong Chen, Lei Deng, Bangyan Wang, Guoqi Li, Yuan Xie

Powered by our metric and framework, we analyze extensive initialization, normalization, and network structures.

Philosophy

Transfer Learning in General Lensless Imaging through Scattering Media

no code implementations28 Dec 2019 Yukuan Yang, Lei Deng, Peng Jiao, Yansong Chua, Jing Pei, Cheng Ma, Guoqi Li

In summary, this work provides a new solution for lensless imaging through scattering media using transfer learning in DNNs.

Transfer Learning

Adversarial symmetric GANs: bridging adversarial samples and adversarial networks

1 code implementation20 Dec 2019 Faqiang Liu, Mingkun Xu, Guoqi Li, Jing Pei, Luping Shi, Rong Zhao

Generative adversarial networks have achieved remarkable performance on various tasks but suffer from training instability.

Image Generation

Compressing 3DCNNs Based on Tensor Train Decomposition

no code implementations8 Dec 2019 Dingheng Wang, Guangshe Zhao, Guoqi Li, Lei Deng, Yang Wu

However, due to the higher dimension of convolutional kernels, the space complexity of 3DCNNs is generally larger than that of traditional two dimensional convolutional neural networks (2DCNNs).

Hand Gesture Recognition Hand-Gesture Recognition +3

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

1 code implementation3 Nov 2019 Lei Deng, Yujie Wu, Yifan Hu, Ling Liang, Guoqi Li, Xing Hu, Yufei Ding, Peng Li, Yuan Xie

As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency.

Model Compression Quantization

BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS

no code implementations25 Sep 2019 Faqiang Liu, Mingkun Xu, Guoqi Li, Jing Pei, Luping Shi

Generative adversarial networks have achieved remarkable performance on various tasks but suffer from sensitivity to hyper-parameters, training instability, and mode collapse.

Image Generation

DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking

no code implementations15 Sep 2019 Zheyu Yang, Yujie Wu, Guanrui Wang, Yukuan Yang, Guoqi Li, Lei Deng, Jun Zhu, Luping Shi

To the best of our knowledge, DashNet is the first framework that can integrate and process ANNs and SNNs in a hybrid paradigm, which provides a novel solution to achieve both effectiveness and efficiency for high-speed object tracking.

Object Tracking Open-Ended Question Answering

Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers

2 code implementations5 Sep 2019 Yukuan Yang, Shuang Wu, Lei Deng, Tianyi Yan, Yuan Xie, Guoqi Li

In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency.

Quantization

A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks

1 code implementation2 Jul 2019 Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, Kay Chen Tan

Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.

Event-based vision

Deep Spiking Neural Network with Spike Count based Learning Rule

no code implementations15 Feb 2019 Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li, Haizhou Li

Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synchronous analog counterpart.

Test

HitNet: Hybrid Ternary Recurrent Neural Network

no code implementations NeurIPS 2018 Peiqi Wang, Xinfeng Xie, Lei Deng, Guoqi Li, Dongsheng Wang, Yuan Xie

For example, we improve the perplexity per word (PPW) of a ternary LSTM on Penn Tree Bank (PTB) corpus from 126 (the state-of-the-art result to the best of our knowledge) to 110. 3 with a full precision model in 97. 2, and a ternary GRU from 142 to 113. 5 with a full precision model in 102. 7.

Quantization

Batch Normalization Sampling

no code implementations25 Oct 2018 Zhaodong Chen, Lei Deng, Guoqi Li, Jiawei Sun, Xing Hu, Xin Ma, Yuan Xie

In this paper, we propose alleviating this problem through sampling only a small fraction of data for normalization at each iteration.

Computational Efficiency

Dynamic Sparse Graph for Efficient Deep Learning

no code implementations ICLR 2019 Liu Liu, Lei Deng, Xing Hu, Maohua Zhu, Guoqi Li, Yufei Ding, Yuan Xie

We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference.

Dimensionality Reduction

Direct Training for Spiking Neural Networks: Faster, Larger, Better

no code implementations16 Sep 2018 Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi

Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention.

Crossbar-aware neural network pruning

no code implementations25 Jul 2018 Ling Liang, Lei Deng, Yueling Zeng, Xing Hu, Yu Ji, Xin Ma, Guoqi Li, Yuan Xie

Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operations.

Network Pruning

L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks

no code implementations27 Feb 2018 Shuang Wu, Guoqi Li, Lei Deng, Liu Liu, Yuan Xie, Luping Shi

Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs).

Computational Efficiency Quantization

Training and Inference with Integers in Deep Neural Networks

2 code implementations ICLR 2018 Shuang Wu, Guoqi Li, Feng Chen, Luping Shi

Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics.

Continual Learning

Super-resolution of spatiotemporal event-stream image captured by the asynchronous temporal contrast vision sensor

no code implementations7 Feb 2018 Hongmin Li, Guoqi Li, Hanchao Liu, Luping Shi

Firstly, the event number of each pixel of the HR DVS image is determined with a sparse signal representation based method to obtain the HR event-count map from that of the LR DVS recording.

Event-based vision Super-Resolution

Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks

1 code implementation8 Jun 2017 Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi

By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology.

object-detection Object Detection +1

GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework

1 code implementation25 May 2017 Lei Deng, Peng Jiao, Jing Pei, Zhenzhi Wu, Guoqi Li

Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github. com/AcrossV/Gated-XNOR.

Real-time Tracking Based on Neuromrophic Vision

no code implementations18 Oct 2015 Hongmin Li, Pei Jing, Guoqi Li

Neuromorphic vision is a concept defined by incorporating neuromorphic vision sensors such as silicon retinas in vision processing system.

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