The Spiking Neural Network (SNN) is a biologically inspired neural network infrastructure that has recently garnered significant attention. It utilizes binary spike activations to transmit information, thereby replacing multiplications with additions and resulting in high energy efficiency. However, training an SNN directly poses a challenge due to the undefined gradient of the firing spike process. Although prior works have employed various surrogate gradient training methods that use an alternative function to replace the firing process during back-propagation, these approaches ignore an intrinsic problem: gradient vanishing. To address this issue, we propose a shortcut back-propagation method in our paper, which advocates for transmitting the gradient directly from the loss to the shallow layers. This enables us to present the gradient to the shallow layers directly, thereby significantly mitigating the gradient vanishing problem. Additionally, this method does not introduce any burden during the inference phase. To strike a balance between final accuracy and ease of training, we also propose an evolutionary training framework and implement it by inducing a balance coefficient that dynamically changes with the training epoch, which further improves the network's performance. Extensive experiments conducted over static and dynamic datasets using several popular network structures reveal that our method consistently outperforms state-of-the-art methods.
ClearGCD: Mitigating Shortcut Learning For Robust Generalized Category Discovery
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Loughborough University（洛桑大学）
Beyond Shortcuts: Mitigating Visual Illusions in Frozen VLMs via Qualitative Reasoning
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥国家科学中心）；Anhui Polytechnic University（安徽理工大学）；Hefei University of Technology（合肥工业大学）；Anhui University（安徽大学）；IGS, Imperial College London（帝国理工学院伦敦分校）
Mitigating Backdoors via Decoy Shortcuts and Knowledge Decoupling
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles
Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Eberhard-Karls-Universität Tübingen（埃伯哈德卡尔斯蒂宾根大学）；Idiap Research Institute（伊迪亚普研究所）
Brain-Inspired Multimodal Spiking Neural Network for Image-Text Retrieval
Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology（武汉理工大学交通物联网湖北省重点实验室）；State Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理全国重点实验室）
SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection
School of Software, Liaoning Technical University（辽宁工程技术大学软件学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；University of Electronic Science and Technology of China（电子科技大学）
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
School of Computing, University of Georgia（佐治亚大学计算学院）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Georgia（佐治亚大学）；Department of Physics and Astronomy（物理与天文学系）
Improving Robustness to Multiple Spurious Correlations by Multi-Objective Optimization
POSTECH（浦项科技大学）；Department of Computer Science and Engineering, POSTECH（浦项科技大学计算机科学与工程学院）；Graduate School of Artificial Intelligence, POSTECH（浦项科技大学人工智能研究生院）
CSDformer: A Conversion Method for Fully Spike-Driven Transformer
Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization（浙江3D微/纳加工与表征重点实验室）；Westlake Institute for Optoelectronics（西lake光电研究所）；CenBRAIN Neurotech（CenBRAIN神经科技）；School of Engineering（工程学院）；Westlake University（西lake大学）
