no code implementations • 13 Jan 2019 • Hussein A. Al-Barazanchi, Hussam Qassim, David Feinzimer, Abhishek Verma
The outcome result from the two datasets proved our proposed model (Residual-CNDS) effectively handled the slow convergence, overfitting, and degradation.
no code implementations • 15 Jun 2017 • Hussam Qassim, David Feinzimer, Abhishek Verma
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.
no code implementations • 5 May 2017 • Hussam Qassim, David Feinzimer, Abhishek Verma
This model can be implemented on almost every neural network model with fully incorporated residual learning.
no code implementations • 7 Aug 2016 • Hussein A. Al-Barazanchi, Hussam Qassim, Abhishek Verma
In the other side, Residual Learning is another technique emerged recently to ease the training of very deep CNN.