| Abstract: |
Object recognition is an important problem in Computer Vision with many applications such as image search,
autonomous car, image understanding, etc. In recent years, Convolutional Neural Network (CNN) based models
have achieved great success on object recognition, especially VGG, ResNet, Wide ResNet, etc. However,
these models involve a large number of parameters that should be trained with large-scale datasets on powerful
computing systems. Thus, it is not appropriate to train a heavy CNN with small-scale datasets with only
thousands of samples as it is easy to be over-fitted. Furthermore, it is not efficient to use an existing heavy
CNN method to recognize small images, such as in CIFAR-10 or CIFAR-100. In this paper, we propose a
Lightweight Deep Convolutional Neural Network architecture for tiny images codenamed “DCTI” to reduce
significantly a number of parameters for such datasets. Additionally, we use batch-normalization to deal with
the change in distribution each layer. To demonstrate the efficiency of the proposed method, we conduct experiments
on two popular datasets: CIFAR-10 and CIFAR-100. The results show that the proposed network not
only significantly reduces the number of parameters but also improves the performance. The number of parameters
in our method is only 21.33% the number of parameters of Wide ResNet but our method achieves up
to 94.34% accuracy on CIFAR-10, comparing to 96.11% of Wide ResNet. Besides, our method also achieves
the accuracy of 73.65% on CIFAR-100. |