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brain-inspired multimodal learning based on neural networks

Translational Neuroscience and Clinics 2018年第1期4卷 61-72页

作者： Chang Liu Fuchun Sun Bo Zhang Department of Computer Science and Technology Tsinghua University

Modern computational models have leveraged biological advances in human brain research. This study addresses the problem of multimodal learning with the help of brain-inspired models. Specifically, a unified multimodal learning architecture is proposed based on deep neural networks, which are inspired by the biology of the visual cortex of the human brain. This unified framework is validated by two practical multimodal learning tasks: image captioning, involving visual and natural language signals, and visual-haptic fusion, involving haptic and visual signals. Extensive experiments are conducted under the framework, and competitive results are achieved.

关键词： multimodal learning brain-inspired learning deep learning neural networks

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Denoised Internal Models:A brain-inspired Autoencoder Against Adversarial Attacks

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Machine Intelligence Research 2022年第5期19卷 456-471页

作者： Kai-Yuan Liu Xing-Yu Li Yu-Rui Lai Hang Su Jia-Chen Wang Chun-Xu Guo Hong Xie Ji-Song Guan Yi Zhou School of Life Sciences and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Center for brain Science and brain-inspired Technology Shanghai 201602China School of Life Sciences Tsinghua UniversityBeijing 100084China National Engineering Laboratory for brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-intelligence Nanophotonics School of Optical-electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest *** by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this *** the pipeline in the human brain for visual signal processing,DIM adopts a two-stage *** the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the *** by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each *** evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.

关键词： brain-inspired learning autoencoder robustness adversarial attack generative model

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