TBNN: totally-binary neural network for image classification

Qingsong ZHANG; Linjun SUN; Guowei YANG; Baoli LU; Xin NING; Weijun and LI

doi:10.1007/s11801-023-2113-2

[1] HE R, SUN S, YANG J, et al. Knowledge distillation as efficient pre-training: faster convergence, higher data-efficiency, and better transferability[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 19-24, 2022, New Orleans, Louisiana, USA. New York:IEEE, 2022: 9161-9171.

[2] ZHANG L, CHEN X, TU X, et al. Wavelet knowledge distillation:towards efficient image-to-image translation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 19-24, 2022, New Orleans, Louisiana, USA. New York:IEEE, 2022: 12464-12474.

[3] ZHONG Y, LIN M, NAN G, et al. IntraQ:learning synthetic images with intra-class heterogeneity for zero-shot network quantization[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 19-24, 2022, New Orleans, Louisiana, USA. New York:IEEE, 2022:12339-12348.

[4] LIU C, DING W, XIA X, et al. Circulant binary convolutional networks:enhancing the performance of 1-bit DCNNs with circulant back propagation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 13-19, 2019, Long Beach, CA, USA. New York:IEEE, 2019:2691-2699.

[5] LIU Z, SHEN Z, SAVVIDES M, et al. ReActNet: towards precise binary neural network with generalized activation functions[C]//European Conference on Computer Vision, August 23-28, 2020, Virtual. Cham: Springer, 2020:143-159.

[6] ZHOU S, WU Y, NI Z, et al. Dorefa-net:training low bit width convolutional neural networks with low bit width gradients[EB/OL]. (2016-06-20) [2022-06-22].https://arxiv.org/pdf/1606.06160.pdf.

[7] DING R, CHIN T W, LIU Z, et al. Regularizing activation distribution for training binarized deep networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 13-19, 2019, Long Beach, CA, USA. New York:IEEE, 2019: 11408-11417.

[8] HOWARD A G, ZHU M, CHEN B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017-04-17) [2022-06-22].https://arxiv.org/pdf/1704.04861.pdf.

[9] ZHANG X, ZHOU X, LIN M, et al. Shufflenet:an extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-22, 2018, Salt Lake City, UT, USA. New York:IEEE, 2018:6848-6856.

[10] MEHTA S, RASTEGARI M. Mobilevit:light-weight, general-purpose, and mobile-friendly vision transformer[EB/OL]. (2021-10-17) [2022-06-22]. https: //arxiv.org/pdf/2110.02178v2.pdf.

[11] QIN H, GONG R, LIU X, et al. Forward and backward information retention for accurate binary neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 13-19, 2020, Seattle, WA, USA. New York:IEEE, 2020: 2250-2259.

[12] LIU Z, WU B, LUO W, et al. Bi-real net:enhancing the performance of 1-bit CNNs with improved representational capability and advanced training algorithm[C]//Proceedings of the European Conference on Computer Vision, September 8-14, 2018, Munich, Germany. Berlin, Heidelberg:Springer-Verlag, 2018: 722-737.

[13] LIN X, ZHAO C, PAN W. Towards accurate binary convolutional neural network[J]. Advances in neural information processing systems, 2017, 30:345-353.

[14] SU Z, FANG L, GUO D, et al. FTBNN:rethinking non-linearity for 1-bit CNNs and going beyond[EB/OL]. (2010-09-29) [2022-06-22]. https://www.xueshufan.com/reader/3096361616.

[15] KIM H, KIM K, KIM J, et al. Binaryduo:reducing gradient mismatch in binary activation network by coupling binary activations[EB/OL]. (2002-06-05)[2022-06-22]. https://arxiv.org/pdf/2002.06517v1.pdf.

[16] XIE S, GIRSHICK R, DOLLAR P, et al. Aggregated residual transformations for deep neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI, USA. New York:IEEE, 2017: 1492-1500.

[17] CHOLLET F. Xception:deep learning with depth wise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017:1251-1258.

[18] HUBARA I, COURBARIAUX M, SOUDRY D, et al. Binarized neural networks[J]. Advances in neural information processing systems, 2016, 29:4107-4115.

[19] GONG R, LIU X, JIANG S, et al. Differentiable soft quantization:bridging full-precision and low-bit neural networks[C]//Proceedings of the IEEE International Conference on Computer Vision, October 27-November 3, 2019, Seoul, South Korea. New York:IEEE, 2019: 4852-4861.