Галерея 3114368
Галерея 3114368
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Spatial and Channel Versatile Filters
Abstract: This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the dem... View more
This paper introduces versatile filters to construct efficient convolutional neural networks that are widely used in various visual recognition tasks. Considering the demands of efficient deep learning techniques running on cost-effective hardware, a number of methods have been developed to learn compact neural networks. Most of these works aim to slim down filters in different ways, e.g., investigating small, sparse or quantized filters. In contrast, we treat filters from an additive perspective. A series of secondary filters can be derived from a primary filter with the help of binary masks. These secondary filters all inherit in the primary filter without occupying more storage, but once been unfolded in computation they could significantly enhance the capability of the filter by integrating information extracted from different receptive fields. Besides spatial versatile filters, we additionally investigate versatile filters from the channel perspective. Binary masks can be further customized for different primary filters under orthogonal constraints. We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced. Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters, but require less memory and computation cost.
Date of Publication: 21 September 2021
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CONSIDERABLE computer vision applications (e.g., image classification [56], object detection [51], and image segmentation [45]) have received remarkable progress with the help of convolutional neural networks (CNNs) in last decade. From the pioneering AlexNet [34] to the recent ResNeXt [73], the storage of networks is slightly saved, but the classification accuracy has been continuously improved. This performance improvement comes from sophisticatedly designed calculations in these networks, e.g., residual modules in ResNet [20] and inception modules in GoogleNet [57]. These networks are widely used in the scenario of abundant computation and storage resources, but cannot be easily deployed on mobile platforms, such as smartphones and cameras. Taking ResNet-50 [20] with 54 convolutional layers as an example, about 97MB memory is required to store all filters and about 4.1\times 10^9 times of floating number multiplications have to be operated for an image.
2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)
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