Cooperative Initialization based Deep Neural Network Training
This addresses the challenge of optimizing training dynamics and performance for deep learning practitioners, but it is incremental as it builds on existing activation function and initialization techniques.
The paper tackles the problem of improving deep neural network performance by proposing a cooperative initialization method that uses multiple activation functions in the initial training epochs to enhance feature representation and reduce overfitting, resulting in Top-1 accuracy improvements of 2.8% for VGG-16 and 2.1% for ResNet-56 on CIFAR-100.
Researchers have proposed various activation functions. These activation functions help the deep network to learn non-linear behavior with a significant effect on training dynamics and task performance. The performance of these activations also depends on the initial state of the weight parameters, i.e., different initial state leads to a difference in the performance of a network. In this paper, we have proposed a cooperative initialization for training the deep network using ReLU activation function to improve the network performance. Our approach uses multiple activation functions in the initial few epochs for the update of all sets of weight parameters while training the network. These activation functions cooperate to overcome their drawbacks in the update of weight parameters, which in effect learn better "feature representation" and boost the network performance later. Cooperative initialization based training also helps in reducing the overfitting problem and does not increase the number of parameters, inference (test) time in the final model while improving the performance. Experiments show that our approach outperforms various baselines and, at the same time, performs well over various tasks such as classification and detection. The Top-1 classification accuracy of the model trained using our approach improves by 2.8% for VGG-16 and 2.1% for ResNet-56 on CIFAR-100 dataset.