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- W4387545069 abstract "Single Image Super Resolution (SISR) is a common task on devices to enhance quality of visual data. Deep Convolutional Neural Networks (DCNN) have recently shown great results in this field. However, DCNN are not compatible with resource-limited devices, because they demand significant amounts of memory and computations. Binary neural networks (BNN) provide a promising approach to reduce computational complexity and speed up the inference of a model. SISR models are much more vulnerable to degradation in performance when decreasing the precision of weights, than image classification models, due to the complexity of the task that relies on dense pixel-level predictions. Hence, they suffer a significant quality drop when being binarized. The paper investigates the importance of restricting information in BNN and proposes several binary block modifications based on different rescaling mechanisms. We implement different rescaling modules into the binary block and prove them to increase model performance. We propose various binary block modifications and find the most effective rescaling block locations. Our method shows state-of-the-art results, quantitatively proving the importance of applying rescaling in the binary block. Our modifications outperform existing BNN on benchmark datasets, showing the importance of the rescaling mechanism for increasing BNN quality." @default.
- W4387545069 created "2023-10-12" @default.
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- W4387545069 date "2023-01-01" @default.
- W4387545069 modified "2023-10-12" @default.
- W4387545069 title "Study of Rescaling Mechanism Utilization in Binary Neural Networks" @default.
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- W4387545069 doi "https://doi.org/10.1007/978-3-031-44865-2_49" @default.
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