Experimenting novel ideas on deep convolutional neural networks (DCNNs) with big datasets is hampered by the fact that network training requires huge computational resources in the terms of CPU and GPU power and hours. One option is to downscale the problem, e.g., less classes and less samples, but this is undesirable with DCNNs whose performance is largely data-dependent. In this work, we take an alternative route and downscale the networks and input images. For example, the ImageNet problem of 1,000 classes and 1,2M training images can be solved in hours on a commodity laptop without GPU by downscaling images and the network to the resolution of 8 8. We attempt to provide the solution to transfer the knowledge (parameters) of a trained DCNN with lower resolution to improve the efficiency of training a DCNN with higher resolution, and continue training incrementally until the full resolution is achieved. In our experiments, this approach achieves clear boost in computing time without the loss of performance. Read More

#distributed-learning, #machine-learning, #split-learning