  Convolutional neural networks have achieved a great success in the recent
years. Although, the way to maximize the performance of the convolutional
neural networks still in the beginning. Furthermore, the optimization of the
size and the time that need to train the convolutional neural networks is very
far away from reaching the researcher's ambition. In this paper, we proposed a
new convolutional neural network that combined several techniques to boost the
optimization of the convolutional neural network in the aspects of speed and
size. As we used our previous model Residual-CNDS (ResCNDS), which solved the
problems of slower convergence, overfitting, and degradation, and compressed
it. The outcome model called Residual-Squeeze-CNDS (ResSquCNDS), which we
demonstrated on our sold technique to add residual learning and our model of
compressing the convolutional neural networks. Our model of compressing adapted
from the SQUEEZENET model, but our model is more generalizable, which can be
applied almost to any neural network model, and fully integrated into the
residual learning, which addresses the problem of the degradation very
successfully. Our proposed model trained on very large-scale MIT
Places365-Standard scene datasets, which backing our hypothesis that the new
compressed model inherited the best of the previous ResCNDS8 model, and almost
get the same accuracy in the validation Top-1 and Top-5 with 87.64% smaller in
size and 13.33% faster in the training time.
