[1] Zhang X D, Wang T J, Zhu S J et al. Hyperspectral image classification based on dilated convolutional attention neural network[J]. Acta Optica Sinica, 41, 0310001(2021).

[2] Zhang Y P, Zhang C M, Bai J. DenseNet-attention for hyperspectral remote sensing image classification[J]. Journal of Graphics, 41, 897-904(2020).

[3] Wang X L, Girshick R, Gupta A et al. Non-local neural networks[C], 7794-7803(2018).

[4] Guo Y R, Zhang K, Wang X S et al. Image classification method based on end-to-end dual feature reweight DenseNet[J]. Journal of Image and Graphics, 25, 486-497(2020).

[5] Roy A G, Navab N, Wachinger C. Concurrent spatial and channel ‘Squeeze & Excitation’ in fully convolutional networks[M]. Frangi A F, Schnabel J A, Davatzikos C, et al. Medical image computing and computer assisted intervention-MICCAI 2018. Lecture notes in computer science, 11070, 421-429(2018).

[6] Huang S, Li F F, Chen Q. Computed tomography image classification algorithm based on improved deep residual network[J]. Acta Optica Sinica, 40, 0310002(2020).

[7] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).

[8] Shu Y H. Saying goodbye to self attention, Google builds a new kernel Synthesizer for transformer[EB/OL]. https://zhuanlan.zhihu.com/p/148900206

[9] Tay Y, Bahri D, Metzler D et al. Synthesizer: rethinking self-attention in transformer models[EB/OL]. https://arxiv.org/abs/2005.00743

[10] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[11] Chen L L, Zhu H J, Zhu J et al. Multiscale attention model for image classification based on convolutional neural network[J]. Journal of Nanjing University of Science and Technology, 44, 669-675(2020).

[12] Yu F S, Yu J, Lu Y F et al. Gender classification of iris image based on residual network[J]. Laser & Optoelectronics Progress, 58, 1610022(2021).

[13] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[EB/OL]. https://arxiv.org/abs/1706.03762

[14] Zhang Z L, Li L C, Zhu X Q et al. Aspect sentiment analysis combining ON-LSTM and self-attention mechanism[J]. Journal of Chinese Computer Systems, 41, 1839-1844(2020).

[15] Wang Q. Multispectral image fusion and classification based on attention mechanism and complex curvelet fusion network[D](2019).

[16] Cordonnier J, Loukas A, Jaggi M. On the relationship between self-attention and convolutional layers[EB/OL]. https://arxiv.org/abs/1911.03584

[17] Zhu H. Research on text representation and classification based on neural networks and self-attention mechanism[D](2020).

[18] Wang P, Liu R, Xin X J et al. Scene classification of optical remote sensing images based on residual networks[J]. Laser & Optoelectronics Progress, 58, 0210001(2021).

[19] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[20] Qiao S B, Pang S C, Wang M et al. A convolutional neural network for brain CT image classification based on residual hybrid attention mechanism[J]. Acta Electronica Sinica, 49, 984-991(2021).

[21] de Lathauwer L, de Moor B, Vandewalle J. A multilinear singular value decomposition[J]. SIAM Journal on Matrix Analysis and Applications, 21, 1253-1278(2000).

[22] Krizhevsky A, Hinton G. Learning multiple layers of features from tiny images[M]. Handbook of systemic autoimmune diseases(2009).

[23] Fu X, Shen Y T, Li H W et al. A semi-supervised encoder generative adversarial networks model for image classification[J]. Acta Automatica Sinica, 46, 531-539(2020).

[24] Chang D L, Yin J H, Xie J Y et al. Attention-guided Dropout for image classification[J]. Journal of Graphics, 42, 32-36(2021).

[25] Dupont E, Doucet A, Teh Y W. Augmented neural ODEs[EB/OL]. https://arxiv.org/abs/1904.01681

[26] Liao Q L, Leibo J Z, Poggio T. How important is weight symmetry in backpropagation?[EB/OL]. https://arxiv.org/abs/1510.05067

[27] Salimans T, Goodfellow I, Zaremba W et al. Improved techniques for training GANs[EB/OL]. https://arxiv.org/abs/1606.03498

[28] Qi G J. Loss-sensitive generative adversarial networks on Lipschitz densities[J]. International Journal of Computer Vision, 128, 1118-1140(2020).

[29] Dwibedi D, Aytar Y, Tompson J et al. With a little help from my friends: nearest-neighbor contrastive learning of visual representations[C], 9568-9577(2021).

[30] Hassani A, Walton S, Shah N et al. Escaping the big data paradigm with compact transformers[EB/OL]. https://arxiv.org/abs/2104.05704

[31] Shen Z Y, He L S, Lin Z C et al. PDO-eConvs: partial differential operator based equivariant convolutions[EB/OL]. https://arxiv.org/abs/2007.10408