基于注意力机制的手写体数字识别

LI Bo-yan,ZHANG Yong,YUAN De-rong, XIONG Tang-tang,HE Lang

计算机科学（2022）

Cited 0|Views10

Abstract

作为模式识别的重要分支,手写体数字识别正置于前所未有的热潮之下,卷积神经网络也被广泛应用于相关研究.针对手写体数字识别在训练过程中容易出现梯度爆炸和梯度弥散等现象导致图像识别准确率低的问题,提出了一种嵌入CBAM(Convolutional Block Attention Module)注意力模块的模型,用于手写体数字识别.在卷积神经网络中嵌入CBAM注意力模块,分别从通道和空间维度上筛选出有效特征,抑制无关特征,增强特征的表达能力,提高模型的识别准确率.为进一步提高网络识别准确率,在整个网络架构中充分应用BN(Batch Normalization)算法,加快模型收敛,从而加强模型的抗过拟合能力.在MNIST数据集上进行实验,结果表明,嵌入CBAM注意力模块网络的总体识别准确率达到了99.87％,与一些传统的卷积神经网络模型相比,识别准确率有显著提升.

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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