KEYWORDS: Binary data, Education and training, Detection and tracking algorithms, Image processing algorithms and systems, Image classification, Distance measurement, Digital imaging, Data processing, Time metrology
We present FARA, a novel approach for fast approximation of RFD-like descriptors in the context of document retrieval systems. RFD-like descriptors are widely used for document representation, but their computation is expensive, especially for large document collections. Our method is a CPU-friendly gradient maps computation approximation with sequential memory access and integer-only calculations. There are three types of operations that we use: addition, subtraction, and absolute values. It allows us to effectively use SIMD extensions, resulting in an additional increase in the running speed. Experimental results demonstrate that FARA achieves the same accuracy as RFDoc descriptors and significantly reduces the computational overhead. The proposed approach achieves a twofold speed improvement of gradient maps computation and 25% acceleration of overall descriptor computing time compared to the most efficient RFDoc implementation.
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%.
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