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Residual-CNDS for Grand Challenge Scene Dataset
The outcome result from the two datasets proved our proposed model (Residual-CNDS) effectively handled the slow convergence, overfitting, and degradation.
The Compressed Model of Residual CNDS
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.
Residual Squeeze VGG16
This model can be implemented on almost every neural network model with fully incorporated residual learning.
