KEYWORDS: Binary data, Education and training, Matrices, Neural networks, Mathematical modeling, Solar thermal energy, Batch normalization, Reflection, Data modeling, Chemical elements
The paper is devoted to the training of binary neural networks. They reduce the requirements for computing power and memory, which is especially important in conditions of limited resources. To date, binary networks do not provide sufficient recognition quality comparable to the quality of traditional floating-point networks, so the development of more efficient methods of training networks are highly relevant. In this paper, we propose a probabilistic model of a neural network that can be transformed into a binary network and consider a way of binarization. Experimental results have shown that our model with incremental binarization and subsequent fine-tuning makes it possible to achieve recognition accuracy of 97.5% for MNIST image classification problem when the accuracy of the binary model trained by Straight Through Estimation was 87.5%.
There are various techniques for decreasing the computational complexity of neural networks, and a number of them use neuron approximations. A bipolar morphological neuron is an approximation of a classical neuron that can be used on FPGAs and ASICs to enhance computational efficiency. It uses 4 distinct computational pathways utilizing addition and maximum functions, in contrast to the traditional neuron which employs multiplication and addition. In this paper, we introduce bipolar morphological YOLO network for object detection task. To train the network, we employ an iterative approach that combines knowledge distillation for backbone and fine-tuning of the network’s head. Our experiments, which were conducted using the COCO dataset, yield results that are on par with classical networks. Specifically, the average recall for large images is 0.393 for the BM network and 0.371 for the classical network. Additionally, the average precision values are 0.088 for the BM network and 0.097 for the classical network. These outcomes establish a baseline for object detection using bipolar morphological networks.
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