Semi-supervised manifold binary neural network construction method under optimal scale

The invention discloses a method for constructing a semi-supervised manifold binary neural network under an optimal scale, which effectively reserves spatial information of data and makes up for information loss by combining an OLSR method with binary convolution, thereby improving the accuracy and efficiency of depth feature extraction. By integrating scale items in the OLSR model, the method can better adapt to data under different scales, so that the generalization ability and adaptability of the model are improved; and a scale self-learning method is adopted to ensure that an optimal solution is obtained in each iteration, so that the convergence speed of the model is accelerated, and the training efficiency is improved. 本发明公开了一种最优尺度下半监督流形二值神经网络构建方法，通过将OLSR方法与二进制卷积结合，有效地保留数据的空间信息，弥补信息损失，从而提高深度特征提取的准确性和效率。通过在OLSR模型中集成尺度项，可以更好地适应不同尺度下的数据，从而提高模型的泛化能力和适应性；采用尺度自学习方法，确保每次迭代都获得最优解，从而加快模型的收敛速度，并提高训练效率。