GoogLe2Net: Going Transverse with Convolutions
- URL: http://arxiv.org/abs/2301.00424v1
- Date: Sun, 1 Jan 2023 15:16:10 GMT
- Title: GoogLe2Net: Going Transverse with Convolutions
- Authors: Yuanpeng He
- Abstract summary: We propose a novel CNN architecture called GoogLe2Net.
It consists of residual feature-reutilization inceptions (ResFRI) or split residual feature-reutilization inceptions (Split-ResFRI)
Our GoogLe2Net is able to reutilize information captured by foregoing groups of convolutional layers and express multi-scale features at a fine-grained level.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Capturing feature information effectively is of great importance in vision
tasks. With the development of convolutional neural networks (CNNs), concepts
like residual connection and multiple scales promote continual performance
gains on diverse deep learning vision tasks. However, the existing methods do
not organically combined advantages of these valid ideas. In this paper, we
propose a novel CNN architecture called GoogLe2Net, it consists of residual
feature-reutilization inceptions (ResFRI) or split residual
feature-reutilization inceptions (Split-ResFRI) which create transverse
passages between adjacent groups of convolutional layers to enable features
flow to latter processing branches and possess residual connections to better
process information. Our GoogLe2Net is able to reutilize information captured
by foregoing groups of convolutional layers and express multi-scale features at
a fine-grained level, which improves performances in image classification. And
the inception we proposed could be embedded into inception-like networks
directly without any migration costs. Moreover, in experiments based on popular
vision datasets, such as CIFAR10 (97.94%), CIFAR100 (85.91%) and Tiny Imagenet
(70.54%), we obtain better results on image classification task compared with
other modern models.
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