Efficient neural network compression via transfer learning for machine vision inspection

Abstract

Several practical difficulties arise when trying to apply deep learning to image-based industrial inspection tasks: training datasets are difficult to obtain, each image must be inspected in milliseconds, and defects must be detected with 99% or greater accuracy. In this paper we show how, for image-based industrial inspection tasks, transfer learning can be leveraged to address these challenges. Whereas transfer learning is known to work well only when the source and target domain images are similar, we show that using ImageNet—whose images differ significantly from our target industrial domain—as the source domain, and performing transfer learning, works remarkably well. For one benchmark problem involving 5,520 training images, the resulting transfer-learned network achieves 99.90% accuracy, compared to only a 70.87% accuracy achieved by the same network trained from scratch. Further analysis reveals that the transfer-learned network produces a considerably more sparse and disentangled representation compared to the trained-from-scratch network. The sparsity can be exploited to compress the transfer-learned network up to 1/128 the original number of convolution filters with only a 0.48% drop in accuracy, compared to a drop of nearly 5% when compressing a trained-from-scratch network. Our findings are validated by extensive systematic experiments and empirical analysis.